Application Design
MSc Product Service System Design2017/2018
Double Degree Program Candidate
Jan Dornig
Scuola del Design Tutor
Davide Fassi
College of Design and Innovation Tutor
ABSTRACT
With advances made in the field of Machine Learning and increasing consumption of digital content, individualisation of digital services are reaching new heights. As the data those algorithms are trained on, is created by human behavior, the algorithms can be seen as a digital mirror of human actions. Currently the control of most of these
algorithms and the data they collect is often innaccessible to users. In this work, we explore the value of using artificial intelligence technology in music discovery and the benefit of giving the user agency over the technology and data. The study examines current music services and relevant literature before reviewing the technological possibilites, necessitites and implications of using machine learning. After
conducting user interviews for a human centered design of the service, the work results in the proposal of a music discovery service with an interactive machine learning system in form of a digital application which uses an interactive knowledge graph for visualization and interactive intelligent agents for exploration.
Keys words: Music Discovery, Artificial Intelligence, Service System,
ITALIAN ABSTRACT
Con i progressi fatti nel campo dell' apprendimento della macchina e l'aumento del consumo di contenuti digitali, l'individualizzazione di servizi digitali sta raggiungendo nuove altezze. Visto che i data su cui questi algoritmi sono prodotti sono basati su abitudini umane, possiamo vederli come degli specchi delle azioni umane. Attualmente il controllo della maggior parte di questi algoritmi e dei dati che raccolgono è spesso inaccessibile agli utenti. In questo lavoro scopriremo l'importanza di usare la tecnologia di intelligenza artificiale nella scoperta musicale e i benefici di dare all'utente potere oltre che la
tecnologia e i dati. Questo studio esamina gli attuali servizi riguardanti la musica e la letteratura attinente, Riesaminando le possibilità
tecnologiche, le necessità e le implicazioni di usare l'apprendimento della macchina. Dopo aver condotto interviste per settare un servizio human centered, lo studio porta alla proposta di un servizio di scoperts musicale con un sistema di apprendimento della macchina interattivo nella forma di applicazione digitale, che usa un grafico di conoscenza interattiva per quanto riguarda l'estetica, e agenti interattivi intelligenti per l'esplorazione
Keys words: Scoperta della musica, intelligenza artificiale, programma di
Contents
1 INTRODUCTION ... 6
1.1 Research Background ... 6
1.2 Research Methodology ... 7
1.3 Research Usefulness ... 8
1.4 Scope – Music Discovery ... 8
2 MUSIC DISCOVERY TOOLS SURVEY ... 12
2.1 Service Benchmarks ... 14
2.1.1 Heuristic User Interface Evaluation ... 15
2.1.2 Spotify ... 17 2.1.3 Pandora ... 32 2.1.4 Last.fm... 39 2.1.5 Allmusic ... 46 2.2 Summary ... 52 3 FUNCTIONS OF MUSIC ... 57
3.1 Music and Identity ... 57
3.2 Self-Reflection ... 60
3.2.1 Music as Therapy ... 61
3.3 Summary ... 62
4 THE INFLUENCE OF TECHNOLOGY ON MUSIC CONSUMPTION ... 63
4.1 Describing Music ... 65
4.2 Music Information and Big Data ... 66
5 HUMAN AUGMENTATION AND AI IN MUSIC DISCOVERY ... 72
5.1.1 Interactive Machine learning... 73
5.1.2 Agentive Technology ... 76
5.2 Current State ... 77
5.3 Collaborative AI ... 81
5.3.2 Machine Learning Algorithms for Recommendation ... 85
6 DESIGN OF A COLLABORATIVE AI-HUMAN MUSIC DISCOVERY SERVICE ... 87
6.1 User Interviews ... 87
6.2 Questions and Answers ... 89
6.3 User Interview Summary ... 101
6.4 Service Positioning ... 103 6.5 User Definition ... 105 6.6 System Architecture ... 106 6.7 Customer Journey ... 108 6.8 Interface Design ... 111 6.9 Validation Interviews ... 119
List of Figures
Figure 1 Spotify UI – Browse ... 23
Figure 2 Spotify UI – Artist ... 24
Figure 3 Spotify Mobile App UI, Home screen (left) Artist screen (right)... 25
Figure 4 Spotify General Music Controls (Highlighted) ... 31
Figure 5 Spotify Discover Weekly Feedback Functionality (Highlighted) ... 31
Figure 6 Spotify Radio Controls and Feedback (highlighted) ... 31
Figure 7 Pandora Web UI ... 35
Figure 8 Pandora Music Style Expectations ... 36
Figure 9 Pandora Radio Controls - Similar Artists & Feedback (Highlighted) ... 37
Figure 10 Last.fm UI – Home ... 42
Figure 11 Last.fm - Listening report ... 42
Figure 12 last.fm Song "Heart" Control (Highlighted) ... 45
Figure 13 Allmusic UI - Home ... 48
Figure 14 Allmusic UI - User profile ... 49
Figure 15 Allmusic Add & Ratings Controls ... 51
Figure 16 Cover Art Style Elements ... 56
Figure 17 Global recorded music revenues - source: ifpi Global Music Report 2018[37] . 67 Figure 18 Use of streaming services - source: ifpi Global Music Report 2018[37] ... 67
Figure 19 Discover Weekly feature and Spotify personal taste profile.[43] ... 71
Figure 20 Genre view ... 112
Figure 21 Search with related artists area ... 113
Figure 22 Search with related artists area and info sign ... 113
Figure 23 Agent options ... 114
Figure 24 Scout Training View ... 115
Figure 25 Scout Information View ... 117
Figure 26 Scout Map View ... 118
Figure 27 Genre view with marker ... 119
List of Tables Table 1 Service Comparison ... 54
1 Introduction
1.1 Research Background
The current climate of AI (artificial intelligence) development and research is favoring rapid advances. Emerging research in this field is fueled by technical progress and substantial investments in the design and development of algorithms and computing. As more and more resources are poured into this technical development, the capabilities of what is widely called Artificial Intelligence, used synonymously to Machine Learning, is increasing fast. On the other hand, Human Computer Interaction (HCI) with AI has not been explored thoroughly yet due to the short time designers and researchers could utilize the products of recent advances. But as we can observe more AI tools becoming available outside of technical personal, the necessity for focused research increases too. The areas of possible research range from concerns about ethics, responsibility and usefulness to emerging new services, user experience and interaction design. Currently artists and other creatives are on the forefront of experiments with interactive AI programs, often making them accessible to the public[23,24,67,68], but there is still a large gap between traditional HCI and emerging possibilities with AI applications.
In this research, we will explore the options to utilize AI for the purpose of music discovery with a focus on personal AI. Personal AI in this regard refers to a user-centric and interactive AI that follows a human-in-the loop approach, creating a conceptual service that gives the power of AI in the hands of the user.
Relevant research and developments connected to this study can be found in different fields. As the work will focus on user’s music habits and
how he can interact with AI including actions, reactions and benefits, a core area on the technological side is existing research in Machine Learning (ML) and Interactive Machine Learning (IML). When looking at music preferences, reflective practices and the user needs, there are various influences from psychological to professional development research. Especially in professional development we can find practices and processes that are meant for efficient application and might favor an adoption and symbiosis with AI technology.
1.2 Research Methodology
This thesis explores the topic by first explaining why the field of music consumption and discovery is of interest in regard to AI applications. To understand the current state of music discovery, a survey of different existing digital music is performed. Following, an examination of existing literature surrounding the topic of music consumption and the underlying functions of music is conducted. After having established the music background of the research, we will discuss the state of AI, agentive technology and possibilities of using a human AI collaborative approach to the topic. The conclusion is done in form of a concept design for a collaborative human-AI music discovery service. The design process first uses in-depth interviews with various possible target users and the discussion of the results as a starting point, before positioning the service, detailing the customer journey and suggesting system architecture and interface design.
1.3 Research Usefulness
The aim of this study is to explore how users can benefit from AI technology that is reflective of an individual user and follows the users interactive input for music discovery. The research will try to find the implications that arise by making a single individual the focus of a specific interactive AI with the goal to replicate, represent and or reflect characteristics of this user while serving the user with the resulting behavior.
One can say that any tool that a human creates is a reflection of human needs and with AI this goes much further. The reflection on human behavior is an inherent aspect of AI development. Reflecting on individual and collective human behavior is part of AI, as it is needed to build software which acts what humans describe as intelligent. Within the before mentioned goals, it will be necessary to determine what behavior, knowledge, believes or similar can be learned by AI and what are the necessities to do so, while on the other hand looking at possible applications and user benefits.
Advancing knowledge of this field and understanding the implications this has for users and designers is important for improving the value AI can generate for users. As this involves working with AI to create a user centered application, some aspects of the project will help understand how designers could or should approach working with AI. Additionally, the work will be able to create awareness for this specific, humanistic aspect of AI in the design community.
1.4 Scope – Music Discovery
After the initial focus on possibilities of using AI for personal purposes, the scope of the study needed to be narrowed. Based on the
findings regarding the need for data availability and human behavior/personality aspects that can be assessed, three possibilities were weighed.
Mental health/ Self Awareness: A focus could be set on the
interaction between the user and a medium using text or voice input. Gathered data could be confidently analyzed using different ML techniques since the formats are well established in the field of AI. In the end it was chosen not to continue with this initial idea. On one hand any practice that would dare to claim health benefits would need scientific backing that is far out of the scope of this study. At the same time, it was found that multiple parties, as the before mentioned replica.ai but also other companies have been making substantial progress in this direction already which would limit the possibility of adding new discoveries in the space.
Gaming: As gaming has been a traditional field of application for AI,
it is reasonable to assume that there are additional possibilities for further advances based on the favorable circumstances that can provide relatively simple user data in a simulated and therefore easier to handle environment. But the purpose of reflecting the user’s behavior would be more reliant on the actual game than on the mechanics of the AI agent. It would be possible to use such as a premise for e-sports or game testing purposes but might prove narrow in possible solutions.
Taste development: With the rise of the smartphone, the digital
consumption of media has risen to new heights. Already digital technologies and the services provided on them, especially social media
services, have been able to capture a high amount of daily attention from people. Famously, all this creates data that companies, like those already mentioned as companies in the AI space, Google and Facebook among other. These are companies that act as intermediates between service/product selling companies and the platform users, serving them extremely targeted advertising to monetize their services which are otherwise free to the end user. This data ranges from the text input and browsing habits of people to video, sound/music and images uploaded and consumed by users to the way users represent themselves on the internet. While the end user enjoys a free service with some of his attention being directed to advertising, it goes to show how much value there is in user data. Especially on social networks users create actionable commercial data en masse. The users not only showcase their own tastes and personality through different means- directly and purposefully, but also add data and information by interacting with many other entities and services on the platform, from companies they “like” to artists or political parties and individuals they follow, shared comments and reviews. Hugo Liu demonstrated in his study of more than 100.000 user profiles of the platform MySpace that this data can be seen as a taste performance of the user. Further can be inferred from his work that the interaction of the user with the platform can have transformational qualities since the public display helps in reinforcing a person’s own notion of who you are and presents a space where experimentation with ones is represented identity is relatively easy and flexible. His findings also led to the decision to focus within this spectrum of taste performance on the aspect of music as he found that” Music interests accounted for the greatest proportion of this vocabulary, with over 70,000 unique music tokens mentioned by at least two users and 15,000 unique tokens mentioned by at least 10 users.”[25]
Taken together, the high amount of data concerning music, and the general widespread involvement of people in this topic as past-time as well as the aspect of music regarding the personality identification of their personality makes it an highly interesting field for design research. As we discuss later on, while people in general nowadays create a high amount of data, music has a fairly open community with some services and many people sharing their information freely.
2 Music Discovery Tools Survey
With the high interest of humanity in the practice of creating and consuming music, the topic of music discovery has attracted much attention over the years. As music invites experimentation, and musicians often strive to differentiate themselves from those who have come before, new technology is often picked up by those musicians and experimented with. As with music generation, over the years also music discovery has gone through many stages. Only about a hundred years ago, recorded music was still in its early stages and most music was, or better had to be consumed live. At the same time, the consumer industry as we know it today was only starting to take hold, bringing such entertainment to the masses. The development of the radio and the gramophone was a revolution for music consumption. Suddenly people didn´t need a live musician to listen to music, which made it much more accessible. Especially in the United States, where much of these developments took place, the industry changed fast. Now people in areas that never have seen a particular musician visiting, were still able to listen to them whenever they wanted. Radio especially developed as a way to discover new music and the radio hosts and DJ´s became the facilitators of this. With their reach over the airwaves, they were able to influence tastes and the underlying music market. New music trends were influenced this way – by local DJ´s broadcasting what was happening to a larger audience. Subsequently to music gaining a wider foothold in the population and becoming an ever-increasing industry, new mediums emerged. Music magazines about specific subcultures and youth culture became a popular format and TV added visuals to music performance broadcasting culminating in the famous American MTV channel. Allmusic, a service we
discuss in the next chapters, was one initiative that created a 1200 pages book, listing as many records as possible when it first came out.[58]
Later on, with the rise of the internet, radio shifted to web radio and more and more informal news sources for music discovery – fans and enthusiast around the world started sharing their tastes and finding online. Through the possibility to share music online in .mp3 format, often these websites and blog actually offered the music for download. Sometimes this was especially interesting since some music recordings were not available commercially, but widespread music piracy was also a result of the internet development.[58] While blogging and social media resemble formats that can be compared to music magazines and radio shows, we can also find other tools now that present new interactive ways to explore the breadth of music. http://everynoise.com, http://liveplasma.com and https://www.music-map.com/ are three websites that feature a map like visualization of music. Everynoise in particular showcases a wide range of genres, their location on the map being representative of musical relations. The visitor is able to listen to one selected soundbite from each genre to get a better idea of what this style sounds like. Liveplasma and music-map on the other hand are asking the visitor for one artist to start from, and subsequently generate a map with the artist at the center and other related artists connecting to it. They both provide a very small subsample of the overall music landscape this way and users can explore this step by step by clicking on other artists, which regenerates the map with the new artist in the middle. While Liveplasma also provides sound samples, music-map does not. Other more playful ways to discover music on the web can be found to, as for example Spotimap, a visualization that uses the map of the earth as background and put songs on the map according to their mention of a geographic location.[59] These webtools
seem interesting and playful in general but seem to lack functionality to use them over a longer period of time for structured exploration.
2.1 Service Benchmarks
As a means to better understand the status quo of music streaming and discovery, this study performs a benchmark analyses of four services – Spotify, Pandora, Last.fm and Allmusic. Each of these services will be introduced and analyzed regarding their backstory, customer promise, business model, features, data collection methods and interface. The services were chosen based on a preliminary survey of available services in which we looked for “benchmark” examples that represent outstanding examples for this study. Important was that the service had aspects and functionalities useful for music discovery and work with data and digital user interaction, to inform as much as possible about the status quo in this field. The wider selection included services such as Youtube Music, Apple Music, Tidal, Deezer, Prime Music (Amazon).
Spotify was chosen for their exemplary use of collaborative filtering, which resulted in features which have been highly praised by users and media, which is strongly in contrast to reviews from Apple Music which is supposed to be lacking in this kind of functionality.
Pandora is an outstanding example for their Music Genome Project, the self-proclaimed “most comprehensive analyses of music ever undertaken”[51], as well as their overall performance as one of the most widely used web radios, at least in the past.
In the wake of the success of streaming service, many other web-based services have struggled to maintain sufficient user numbers to have a valuable business – last.fm is one service that has been struggling with this but is so far still the class-example for music tracking and has
achieved good integration with other services to stay relevant. Compared to other services, last.fm actually shares user data, and statistics, not just for the user directly but also for research and third parties, which makes it another choice for the evaluation.
Allmusic is another interesting case that actually has its roots in pre-internet times, as we will explain. With its mix of online database and music-blogging/news with expert curation and comments, it presents another type of service that makes it standout from purely database-based services.
2.1.1 Heuristic User Interface Evaluation
For the evaluation of the music discovery functionality, we will follow the guidelines for heuristic evaluation as suggested by Nielsen[69] and more recently discussed by Wilson[70] as this method is suitable for the circumstances and provides a flexible framework to tailor the method towards the study´s need of evaluating specific elements without access to actual user reviews that are comparable. We will further focus in general on a task-based approach, centered on the music discovery features of the services while paying additional attention to the interface elements used for user feedback towards music within the service.
The evaluation will be task oriented and the scenario will, adhering to the research topic, revolve around music discovery. The reviewer will in this case log into the applications and try to search for a new artist or song similar to another artist which the user already knows. We will use the band “animal collective” as reference, a band that is fairly known but not completely mainstream. If the reviewer succeeds in this, a secondary task will be done which is to add this new artist to the user’s personal collection,
or otherwise try to bookmark or highlight it for later recollection. The test will be conducted on the desktop versions of the applications for reasons of comparability.
The actual heuristics of the evaluation have been distilled from the literature [69,70,71] in light of the focus of this study. As the exploration of the applications is focused on a specific feature only, we are not concerned with such aspects as robustness of the system, error management or compatibility issues, which are necessary for evaluations of the quality of a working system, but not decisive for the use of a single feature. Although, if a larger problem becomes apparent which noticeably influences the performance of the feature, it would have to be remarked on. The following heuristics were chosen to represent a baseline understanding of what the digital services provide and how they work. Since this study aims to compare different services and gain a general understanding of the workings of these apps, while a more in-depth discussion is held separately on each of them, we are settling on the following four heuristics for the task-based evaluation and comparison.
- Ease of use: Clarity of interface and associations on the path to
accomplish the task - how fast and instinctive can the user grasp what is happening and make further choices. Is there guidance needed and or provided in difficult situations.
- Transparency: This heuristic aims to give a measure of how transparent
and understandable the workings of the app are that lead to the presentation of certain content and other performances.
- Adaptability: To what degree does the application/system adapt towards the user behavior and the user have possibility for personalization of the content or interface.
- Effort: How much perceived effort is needed from the user to arrive at the
set goal.
In addition to the UI evaluation, based on the discovery task, the services will be evaluated in terms of performance regarding: availability of “Discovery Streams”- the different ways to discover music within the tasks ramifications, and the perceived quality of the recommendations received.
The evaluation will be performed by the author after having familiarized himself with the applications. Therefore, this will not be a case where first impressions can be recorded. Following this, the evaluation will not be concerned with initial learnability factors but more interested in estimating how a regular user would perceive the mechanics of the service. To follow the evaluation choices, the study provides a walkthrough with the evaluation.
In addition to the separate evaluations, a summary will be provided at the end of the chapter for easier comparability for the reader.
2.1.2 Spotify
The streaming platform Spotify has been one of the few “tech unicorns” coming from Europe in the last years. This title is given to -mostly internet- technology based companies that have been able to grow at an astonishing rate and disrupt whole industries. Just recently, this company accomplished a sort of economic crowning – it IPO´ed
successfully at the New York Stock Exchange. Spotify currently has a reported revenue of more than 4 billion USD and a market cap of around 28 billion USD with about 170 million users of which about half of them are paying subscribers.[45] Since this development is only a couple of weeks old, it is interesting to observe what is happening at the moment. So can we see, that as Spotify, for the first time, had to publicly announce their economic development numbers and actually disappointed investors partially, the company only a few days later announced changes to their service. Namely to offer more functionality to their “free to use accounts”, lifting some of the restrictions that were reserved for paying members before.[46] According to the company, a more attractive and free entry level will help the company to turn more overall users into paying subscribers in the long run. At the same time, a higher overall number of subscribers makes the service more attractive for advertising and other services, which at the moment is only a very small revenue stream for Spotify, which has been criticized from investors.
In general, it can be said that Spotify follows a Freemium business model. It offers a free entry level which is limited in functionality and supported financially by advertising, while the main profits are generated by a paid membership model. Once a user has been convinced that it is worth to pay about 10 euros per month for the service (approximately 75 rmb), they then unlock extra features for their account, transitioning relatively seamlessly from one to the other. Advertising breaks while listening vanishes and such features as downloading songs for offline listening within the Spotify app. In addition to the standard premium subscription there are a couple of possible discounts like for family accounts or students. Overall a premium user has access to the same
catalogue of music. Although the user is never able to extract a song from the app completely. Which in the eyes of some critics is a concerning development of digital goods, since a company might vanish any time for numerous reasons and could leave users with nothing after years of paying for the access.
The main attraction of Spotify has always been their wide selection of music. Spotify managed as one of the first companies to cut deals with the major music publishing houses to bring their music to a music streaming platform, who have been refusing to do so for years prior. Companies like Napster have famously attempted this before but failed. Still today, with many other services presently trying to corner the music streaming market, Spotify is said to have an advantage over others through a catalog featuring more indie and alternative bands- referring to a genre of music that is widely popular overall but includes many bands that themselves can have a very small following. Although with more than 35 million songs in all the major streaming services nowadays it is difficult to compare this quality without generalizing or selection of very specific genres and sub-genres.[47]
Features
As discussed, Spotify´s main functionality is the ability to browse a library of more than 35 million songs and play the music on-demand, which is streamed from the server to your device in that moment. The user can utilize different devices like a pc/mac, smartphone or even other devices such as smart speakers or gaming consoles. Spotify can be accessed from multiple devices and features native as well as
browser-based access. Therefore, any user can log basically into any device and use the web browser to start streaming.
Additionally, Spotify has partnerships with different companies – for example it connects to Tinder and Facebook, enabling the social sharing of music preferences but the company also works with Uber, enabling to hear your own playlist on a taxi ride.
Apart from the standard search and play functionality, Spotify lets you collect songs in playlists, which is the preferred way for many users. The service created a renaissance for the concept of playlists in this way. Users can share the playlists publicly if wanted and invite friends to edit playlists together. For the social aspect, the app lets you follow other Spotify users or artists. In the beginning of the service it facilitated this network functionality by having users sing up with their Facebook account and replicating their friend’s connections within the Spotify network. A user can then check the public playlists and recently played artists of any other public user or friend.
In terms of music discovery, Spotify facilitates multiple way to find new music.
1. Social. As already mentioned before, Spotify enables users to follow other people’s playlists and even shows the songs friends are currently streaming prominently in the desktop interface.
2. Browse. In the navigation panel, a section called Browse can be found, this leads to an overview site which features a selection of Spotify curated and created playlists that user can listen to and follow – Figure 1. These range from country specific charts to new releases to genre playlists to playlists around specific events happening in the world or season. Within the Browse section, next to the general playlists that are similar for the broader audience is the “discover” section. In this section Spotify presents
the user a personalized selection of artists and songs which are recommended based on past listening behavior. Therefore, the recommended songs and artists come with a tag that says why Spotify recommends this to you, mostly by naming the artist that the music is related to. Besides this feature within Browse, there are categories for podcasts, and local concerts.
3. Discover Weekly. Also, in this section fall the two famous Spotify playlists “Discover Weekly” and “Release Radar”. The former has been especially lauded extensively for their well-received recommendations. Each week a new playlist is generated based on your preferred music. While Release Radar additionally only features recently released music.
4. Radio. While Spotify does not connect actual radio stations, it adapted the concept of radio in the form of on the fly generated music playlists. A user can choose a single artist, album or playlist, or alternatively a genre and hit play. Spotify will then play a constant stream of related artists. While the user can start a “radio” in this way, Spotify also automatically starts playing in this mode if a user is finished listening to any chosen playlist or artist, meaning for example the end of an album is reached – the app then does not stop but simply starts to play the album radio.
In addition to the features in the app, Spotify makes other features accessible in the form of websites in a more experimental fashion. For example, while the standard application features almost no statistics of a person’s past use of the platform, Spotify offers the website www.spotify.me which can show a highly curated summary of the user’s profile, pointing out the top artist and top track and preferred listening times in a day plus the top genres a user listens to. While the functionality is meant for users to enjoy some insights, it is also quite obviously meant
to attract advertising companies. The website even directly shows “Spotify for brands” as the logo and finished ever page with a notice that Spotify can help with building targeted marketing campaigns. Other initiatives like this are for example yearly summaries (year in music) and Spotify SweetSpot, which was a marketing related feature for valentine’s day and would create a custom playlist by connecting two distinct artists through their common related artist and tell you the degree of separation.[48]
Interface
For the analyses of the interface, we will focus on the desktop and mobile app.
Desktop: Spotify uses a dark theme interface which looks a bit
cluttered as can be seen in Figure 1 and Figure 2. It can be separated into 4 different sections. Going from left to right, the desktop app features a section for navigation. On top are the categories Browse and Radio that host the features we discussed before. Underneath is an overview of the Library categories and every playlist a user has to scroll through.
While all the other elements in the interface or more or less static, the main middle area changes according to the selected feature/section/category. In figure 4, the Browse feature is selected, while in Figure 2 an artist is selected. After searching for an artist or through other means, songs/albums/playlists are visible in the middle section and can then be either added to the collection through clicking on the songs and selecting the function, or by drag and dropping the item into a playlist/library category on the left. Additionally, on top of the middle sections respectively left and right aligned are the search input and the access to the user account.
On the right side of the window is the section for “Friend activity” which features a more or less live update of your friend’s activity showing which songs the person listens to. Each user is able to set a profile picture which is visible in this friends feed.
At the bottom of the window is the music player section, it presents the user with the current playing title and the necessary controls. Depending on the mode that the user is in – playlist, radio, discover weekly, these controls slightly change to feature different functionalities. Namely for radio and discover weekly, it enables to user to give the aforementioned feedback towards the songs.
Figure 2 Spotify UI – Artist
Mobile App: The Spotify mobile app follows the design of the desktop
app- Figure 3. It opens to a screen that features the similar functionality as Browse on the desktop with carious curated playlists on the top part of the screen. The lower sections are dedicated to the music player controls and the navigation panel. Through the navigation panel the functions Browse – holding the full functionality like the desktop section, Search, Radio and Library are accessible. Only when the currently playing song is chosen, does the navigation panel disappear. Then the user has to go back to get the controls back, as seen in Figure 3 on the right. In terms of functionality one main difference of the UI´s is that the social section with the current listening of friends is omitted. The user can still access friend’s profiles through the search function.
Figure 3 Spotify Mobile App UI, Home screen (left) Artist screen (right) Heuristic Evaluation & Walkthrough– Artist Discovery
Once the user is logged in, he finds himself on the Browse section of the applications, which serves as a collection of music recommendations from different angles, as described earlier. In our scenario, the seemingly most direct way to arrive at a specific artist would be to use the search bar in the upper part of the applications. In this way it would be a single click plus the typing of the first letters. Halfway through typing the name, the actual band appears as suggestion- “top result”. As the band is not necessarily the most famous band with these letters, it seems that the application favors music which has been listened to before or is part of the user’s playlists. This is later subjectively confirmed by running similar search queries with much less popular bands and having the top result
refer to bands within a user’s collections as top result repeatedly. Another click brings us to the artist page of Animal Collective. If it wouldn´t have been suggested immediately, the user would have only had to finish spelling the full name to see the band suggested as “top result”. This works very well with artist names as they are often unique enough and ranked higher than songs.
At the artist profile, we actually immediately see the rubric of “Related Artists”, where we can explore artists that are apparently similar. This section is positioned prominently on the right side of the top songs of the artist. Unfortunately, in this view there is no other information about these artists and they cannot be directly listened to – meaning that if we would like to listen to a song from this artist, we would have to click on the name and it would take us to that artists page. Though in this way it starts to generate a lot of clicks for the user if he has to go back and forth between the original profile and related artists. At this point there are four similar artists represented for Animal Collective. It seems this is the situation when an artist has a “Merch” section in their profile. “Merch” stands for merchandise and currently shows mostly different albums of a band in a physical vinyl format, ready for purchase. In Figure 2 for example, in the case of the artist “Mr. Scruff”, this “Merch” section is absent and instead shows a slightly different artist overview which includes seven similar artists.
Alternatively, the user can choose to click on the tab “related artists”, also visible in figure 2. This brings the user to a wider range of suggested artists. It starts with the same 4 that were already visible in the overview but continues to list 20 altogether. In this tab, the user can actually listen to the artists directly, without leaving this view. The user is actually presented with three buttons, overlaid over each artist. “Follow”, “Play” and
“More”. It is not clear what concrete effect it has to follow an artist, nothing immediately happens either apart from a change of the icon when this is clicked. “Play” presents a clear command which starts playing the “Top” song of the artist. It is actually also not clear why a song is ranked “Top” within an artists profile. In most cases it seems to coincide with the number of times the track has been played by all users collectively, but since there are some cases where this doesn´t hold true, it is not completely understandable. One could guess that the recent number of plays is stronger weighted than past ones, favoring recent releases, which seems to fit observations, but is speculation. The third button “More” brings up a sub-menu with different options like starting an artist radio or sharing the information through different social channels.
In our scenario, the goal of the task is to discover a new artist upon deciding that the user likes it and then add that artist to the user’s collection. There are multiple options to accomplish this at this stage. By clicking on the former mentioned button “Follow”, the artist would be added to a user’s library but actually the common way for users is to add specific songs of an artist to one of their playlists. To achieve this, the user would most likely choose songs from the artist after listening to them and add them manually. This would happen now as the user is exploring the similar artists one by one, a quiet time-consuming exercise with many manual decisions.
Apart from this manual exploration, the user could also choose to start the bands radio. In this mode a temporary, custom playlist is created, as explained before, which consist of a mix of the original chosen artists songs with these similar/related musicians. The mix seems to be about 4:1 and the playlist would actually continue to play as long as the user desires as it is dynamically added on. Here again, the user would most
likely either directly find a song that he enjoys and add it to a playlist, or upon discovering an interesting artist, listen to that more specifically but ultimately performing the same action.
In addition to the already mentioned possibilities to find related artists, also the “about” tab in the artist overview can lead to finding related artists. While the former mentioned related artists are merely put on the screen without any further explanation of how they are connected, especially since the word “related” can many more things than just a similar sounding artist, this tab shows two more categories with related artists within context. The first part is the written biography of the artist or band, wherein the user can often find the mentioning of other artists and how they have either influenced each other, collaborated or otherwise crossed paths. The second part is a list of playlists wherein the band can be found, and which mostly is mixed also with other artists, although it can happen that single playlists consist exclusively of the original band. Interestingly, Spotify seems to have chosen to favor their company curated playlists over user generated ones and for Animal collective this makes 3 out of 5 playlists. After double checking with a random selection of artists, this seems to be generally the case, with some bands having exclusively Spotify playlists in this section. Again, the user would have to click through the links to where they listen to a song and add either the artist, the album or the song to their library or playlists.
Following a summary of the impressions regarding each heuristic:
- Ease of use: The interface is easy to understand in general and
invites certain actions like a quick click on the play button to play through the more popular songs. It becomes more cumbersome when a user is interested in more than a general presentation of related artists and the
only way to explore is to actually listen through the artists work. Some of the functions like “follow” are prominently displayed are actually not clear in what consequences a “follow” action has over for example simply adding a song to the library. Also splitting functionality over several tabs and within similar information presentation having inconsistent interactions – like the different possibilities of the features related artists versus the related artists tab makes, has a slightly inconvenient feel.
- Transparency: Many aspects of the suggestions that are
presented to the user are not clear. Spotify presents the user with final relationships but it´s hard to tell if the artists share specific genre elements or other similarities that make them related. Overall they quite clearly are not random relations but can be generally seen as musically similar, but since this is not clear until either the user listens to them or reads their biography, this presents a small hurdle that puts the burden on the user.
- Adaptability: The adaption of the search query results to the user’s
past behavior performs very well but results within artists are not. There is no indication if any of the shown artists could be more interesting to the user or other personalized adjustments.
- Effort: There is a big disparity between the first moment a new
artist is recommended, and the time needed to wade through different artists since no additional information is given that would help filter. We can see how many users would be happy to either follow the more curated ways to discover new artists through listening, or rather are open to spending the necessary time to be involved in the search, but the interface and functionality itself seems fitted to the former.
Feedback Collection
As this thesis´s focus is concerned with music consumption and discovery and aims to find a way of purposefully structuring the music discovery process through utilizing new technologies such as user-reflective AI and AI agents, we want to point out the features through which Spotify, and lather the others, are collecting user information and preferences.
Spotify collects all the data on what a person streams, but it only counts a song as “listened” to after more than 30 seconds have been played. If the user interrupts the song before, it is not counted.[49] As every play that is counted factors into a user’s taste profile, exploring songs that a user doesn´t end up liking is not clearly differentiated from songs a user likes as long as both are listened to more than 30 seconds – which is rather short.
The user doesn´t have much agency over this in terms of controlled input. In this way Spotify actually forces the whole system to act on the user behavior rather than controlled input. Although one could argue that all this is controlled input anyway in a digital service. Especially moving songs to the library and into playlists is more reflective of what the user actually listens to than any inconsistent rating input. This is especially true for any “normal” plays, meaning every play outside of the “discover weekly” playlist and radio mode. For the “normal” plays, the system provides a button to quickly add the song to the library - Figure 4, while interestingly Spotify uses different feedback mechanism in each of the two other modes. In “Discover weekly” the user can “heart” or dislike a song- Figure 5. While the heart button gives positive feedback, the dislike button triggers a negative response and lets the user add if this applies towards the song or all of the artist’s work. If negative feedback towards a song in
this playlist is given, the app lets the user know that this song or artist will be avoided in the future for the playlist.
Figure 4 Spotify General Music Controls (Highlighted)
Figure 5 Spotify Discover Weekly Feedback Functionality (Highlighted)
Figure 6 Spotify Radio Controls and Feedback (highlighted)
In radio mode “thumbs up” or “thumbs down” are used for rating music played. All the “liked” songs are then collected in a “radio favorites” playlist for later reference- Figure 6.
While of course Spotify collects every single played song in form of data, the lack of negative feedback for most of the listening on Spotify makes it hard to judge what Spotify thinks about your taste profile in detail. Even within the modes where it is possible to give feedback, it is hard to judge how this effects the overall process. Also, it is not really possible to tell Spotify that you like or dislike certain musical genres or elements.
In general, the most important feedback for Spotify is the listing of songs in user created playlists. Spotify is not only able to know that the user has a heightened interest in a song because he put it in the playlist
but can now know which other songs this one is grouped with and make use of collaborative filtering. This approach is quite often used in platforms with enough users, for example Amazon recommends you possible other objects of interest based on how your purchase matches with other people that did the same. At the same time experts criticize that this leads to a circle of self-reinforcement where generally popular songs become more and more popular and recommended since every placement in a playlist is reinforcing the status in the recommendation algorithm, which can lead to a severe disadvantage for new releases and less known bands, favoring popularity over actual fit.
2.1.3 Pandora
Pandora, or Pandora Internet Radio, is just that, an online service that acts as a web radio. Similar though to the Spotify radio feature, Pandora does not employ human radio hosts that guide you through their shows and playlists. Rather, software is in charge of choosing songs individually for each of their human users in real-time depending on their choices. To be able to deliver unparalleled performance of this recommendation system, the service has developed their own ambitious initiative to categorize music, called the “the most comprehensive analysis of music ever undertaken.”[51] by the company- the music genome project.
Responsible for the start of Pandora is Tim Westergren, a musician who spend years honing his craft and working various music related jobs before presenting his idea to a friend who helped him with the business side of realizing it. Interesting for this thesis, the original circumstances of what made Westergren realize the potential of personalizing music was when he worked as a film music composer. Westergren needed to understand the film directors vision of what the movie and its music
should portray. To do this, he started building “musical Myers-Briggs” profiles for the director, analyzing fitting musical pieces and understanding nuanced preferences and tastes. Through analyzing the music, he would be able to connect new music, new discoveries to pieces that have become before – which then grew into the music genome project and Pandora.[54]
This started already during the internet boom in 2000, at this moment called Savage Beast Technologies and listed at the New York Stock exchange about eleven years later, valued at 2.6 billion USD. At this moment Pandora already had almost a hundred million users.[52] Since 2013 though, the company has been struggling to maintain their early success and is now down to 81 million active users and a revenue of 1.38 billion USD.(data from 2016). Nonetheless, the company employs 2200 people in 26 locations.[50] At this moment in time Pandora is actually only available in the USA, having ceased operations in other countries like Australia and New Zealand. Compared to other Streaming services which boast more than 30 million songs, Pandora used to only have a catalog of around 2 million songs but expanded to a similar broad catalog when they launched Pandora Premium in 2017.
Features
Pandora had for most of their presence a very dedicated web player functionality only, as can be seen in figure 4- with the main feature of personalized radio stations starting from the users past listening behavior in general or specific choices of artists or albums. Pandoras claim of superiority over other, similar, services are based on a self-proclaimed superior recommendation algorithm. It currently gives you the options of playing a “radio”, exploring related artists and reading a small biography
about the artist. Pandora can be accessed from multiple devices, with the browser, desktop and smartphone being the main devices but also through products like Roku streaming devices.
With the introduction of Pandora Premium the service moved more towards functionality similar to other on-demand services. In the wake of the premium service launch, the company made the following statement:
“Sequencing is such an important part. It’s not just grabbing the right music to put into a playlist for you, it’s also organizing it and sequencing it so it flows,” Phillips says. “Those are really important qualities when you want to have a listening experience that just works.[53]
While most services calculate which songs you are likely to enjoy, it seems that Pandora expands on that by actually spending resources on getting the sequence right when songs are played.
User Interface
Compared to the Spotify interface, Pandora presents its users with a very reduced screen. Album artwork is prominently featured in the center of the screen and while the app uses a single strong color with slight white accents and gradation, the overall impression is a minimal style. The remaining screen space is taken by recommended artists on the very left part, a search bar in the center to and similar to Spotify and other music players with the music controls at the bottom of the screen. Next to the controls are also the feedback mechanisms – thumb up and thumb down button. In the upper right corner, the user can visit his account. During listening, the user can choose to click on some of the possible information
and explore other artists, whose information will then be displayed centrally instead of the album artwork.
Figure 7 Pandora Web UI
Heuristic Evaluation & Walkthrough– Artist Discovery
In Pandora, the user starts with typing the band, in our case “Animal Collective” into the search bar. He can then either start playing the artists “station” or go to the artists profile page. In the case of going to the artists profile, the user will firstly see the biography of the artist which actually often includes references to other bands which can be followed to discover new artists. Further, after scrolling down through other information about the artist, the user will find the “similar artist” category which seems to consistently feature six artists.
Similarly, if the user chooses to start a station with the original artist, he would see the information on Figure 1 and been able to scroll down from the station view during a song from animal collective and arrive at the same “similar artist” view. Although, it is important to note that actually the playing of the station in itself is to listen to and consequently discover music that follows the chosen artist in some way. Still, the rather short and lackluster presentation of similar artists casts an unfavorable light on the discovery functionality. In terms of the performance of the radio, Pandora actually provided a textual description of what the radio based on Animal collective will sound like and that the first song is an example of that, as shown in Figure 8.
Figure 8 Pandora Music Style Expectations
The actual songs being played on the radio seemed to be from a much broader choice of music, branching off to other genres, which in some cases was quite interesting, revealing some surprising choices, but in others seemed to go towards mainstream bands that were not reflective of the intended direction.
The best way to accomplish the final part of the task, the digital note-taking of the new artist, is not very clear. It seems like the best way would be to start a new station based on the new artist, but at the same time the user is left wondering if this is the only way. This adds the artist in form of an artist station to a user’s personal list of stations, but it feels a bit like a workaround for the original intention.
Figure 9 Pandora Radio Controls - Similar Artists & Feedback
(Highlighted)
- Ease of use: The pandora application is heavily focused on the web radio
functionality. Discovering an artist in another way is secondary to that. Therefore, it is not surprising to see in the execution of the task some inconveniences, as long as the user doesn´t want to explore through directly listening to the radio. As the functionality is very focused the interface itself is easy to understand but some familiar aspects that are common in other music applications are missing that users have come to expect, like a way of “collecting” artists or songs for future reference. There were some moments when elements like a back button are missing and the user is required to click on pictures to accomplish that, which is not immediately clear when using it.
- Transparency: The information mentioned in Figure 8 provides interesting
mostly presented with particular results without much context explaining choices. The system collects likes/dislikes and displays them for the user when he visits a particular bands profile and is expected to act on those, but as with some bands radio suggestions, once the system is not performing well, it is not clear what causes the issue or what can be done to better performance.
- Adaptability: The system is geared towards presenting the user with
actual personalized content which pairs a “starting point” chosen by the user, like a particular artist, with the past behavior to refine songs suggestions. This in itself is a very adaptive system. On the interface we can find some adaptive content like next to the saved stations of the user were recommendations for other artists are made. Other than that, the interface is rather static as far as adaption to the user behavior is talked about.
- Effort: As the exploration options of similar artists are limited by either the number of artists that are available, or the way of not being able to freely listen to songs, the tool is demanding more mental effort and motivation for a user to do it this way.
Feedback Collection
As Eric Bieschke, chief scientist at Pandora in 2013 already described, users will start with an artist, which is the main indicator, then, especially at the beginning of a person’s use of Pandora, any interactions by the user will be matched to similar decisions from other users who have come before, trying to supplement data. Bieschke says that already three interactions can provide valuable insights this way. He further explains that besides choosing the artists, the “thumbs up” and “-down” (visible in Figure 9) and skipping a song- are the main feedback mechanisms. He
adds that a “thumbs down” carries more weight than a skip, since a skip is ambiguous- a user might like a song but isn´t in the mood or just heard it too recently. Pandora also acts device sensitive and adjusts choices based on the knowledge if a client uses a phone or is in his car.[55] In a more recent interview, Erik Schmidt, Senior Scientist at Pandora commented on the upcoming use of ML inside Pandora:
“One of the most fascinating areas of research at Pandora is Machine Listening. In the music domain, we seek to develop systems that are capable of automatically understanding the musicological content of an audio signal. These systems rely heavily on supervised machine learning, and Pandora’s Music Genome Project provides the largest and most detailed corpus in the world for performing this work, spanning over 1.5 million analyzed tracks. As a result of this dataset, we have been able to develop incredibly rich and accurate machine listening representations.”[56]
2.1.4 Last.fm
Last.fm is a service particularly geared to music discovery and tracking, different from the before mentioned services in that it doesn´t actually host its own music streams. But users are able to connect their last.fm account to many of the other on-demand streaming services such as Google Music, Spotify, Tidal, Deezer, etc. Last.fm seems to try to aggregate the music people listen to from different sources this way and it is able to act as actual interface to the services. So can a user start a recommended song through the last.fm website in the browser and have Spotify play the song locally.
Similar to Pandora, last.fm has a long history for a web company, with the start in 2002 in the UK. Although it currently doesn´t host any songs, it
actually used to have a big catalog as well as a radio service. Changes like this have occurred over the years and are a testament to the difficulty in monetizing a music streaming service.[65][66]
Features
Figure 10 shows the desktop browser interface on a subscriber account. While most functionality is free, the paid subscription account provides an ad-free interface and extended statistic functionality. But in general, the experience is now vastly different between paid and free user. As visible in Figure 10, the main functionality and purpose of last.fm is music recommendation, as that is what the “home” page features prominently. Last.fm provides a mostly collaborative filtering- recommendation approach, which recommends you music based on other users listening behavior. It mostly recommends either an artist, an album or a track.
Second is the tracking functionality, which can be seen in Figure 11. Last.fm provides a user with multiple statistics including: Nr. of songs per day/week/month, total listening time, count of unique artists/albums/tracks, most played genres, listening time of day, nr. of newly played artist/album/track, percentage of “mainstream” fit, as well as ranking in social leaderboards that track things like nr. of “discovered” artists and overall listens.
Compared to Pandora and even Spotify, these social feature, like the leaderboards are more elaborate. Last.fm not only lets you connect with other users and follow them, but it also shows you other people, strangers, who fit your music, calling them simply Neighbors. They are shown with name and music you have in common to encourage you to check their profile to discover what else they listen to. Additionally, users can leave
comments at the artist/album/track page. And can write messages to each other, check out concerts, charts and featured musicians.
When a user searches for an artist/album/track and it is in the catalog, he will be able to see information and statistics about it, like – how many listeners with how many plays the artist has on last.fm, what genres the artist belongs to, which are the most played tracks/albums of the artist, related artists and if available a short biography. The user is also free to add information. Additionally, the user can find photos of the artist, event information and links to YouTube videos. At the same time, all tracks on last.fm have a link to purchase the work – mostly iTunes, Amazon or eBay.
In addition to the possible connection of services mentioned before, last.fm provides desktop apps and browser extensions for you to ensure that every song you want to track, can be added to your last.fm database.
Interface
The last.fm web interface follows typical web styles with the main navigation in the header, where also the music controls are located. The feel of navigating it is very familiar in this way but the overall side can be very crowded. Some pages are quite loaded with information, which makes it in turn hard to find at a glance what you want.
Figure 10 Last.fm UI – Home
Heuristic Evaluation & Walkthrough– Artist Discovery
Upon logging into the platform, the user finds himself at the home view, Figure 10, in this view there is a slight chance to actually encounter suggestions of the chosen artist, especially if the user has been listening to them recently and through last.fm or one of the connected services, for example Spotify. In the likely case this doesn´t happen, as was the reality in this evaluation, the users most direct path to the artists profile would be to press the search icon in the navigation bar and enter the name of the band. After entering the name, the user is redirected to a new page showing the possible matches for the band name with artists ranking first, followed by albums and songs.
While showing the artist as the first match, at this point the results for the artist name feature a lot of collaborations of the original artist with other bands, ranked by popularity. Also a possible way to explore related artists. Popularity on last.fm mostly refers to how many listeners a band has. In this instance, the collaboration is treated just as an artist, with a profile complete with written information, songs, other related artists and such.
At the actual artist profile, the users are presented with a comparable large amount of information which can be used to facilitate exploration from here. The profile features associated genre tags, other users that listen to this band extensively, written information about the band with references to other musicians, as well as similar artists (4), all which lead to overviews or information of what can be considered related music. Though the four similar artists mentioned on the page are actually all projects directly from the band members either solo or as collaborations with others. The profile even features a button dedicated to “play similar
artists” – a function in effect very similar to the Spotify artist radio or pandora artist station.
In addition to these option on the main profile page, the user can choose to open the “similar artist” tab and finds there a page featuring 15 related artists, starting mostly with bands with direct involvement of the original band members but also others. It is worth noting that this is only the first page and the user can navigate through a total of 17 pages of related artists for animal collective.
Once the user has chosen one of these options and listened to a new artist, last.fm automatically records the behavior and the user will find the band from now on in the history of their use-data. To emphasize a song that a user particularly enjoys, he has the option to “heart” a song as seen in Figure 12, which is similar to “liking” or upvoting actions in other software or digital services. In general though, a user is supposed to merely listen to songs to build up their library of listening history.
This is emphasized in the way the service presents the users “library”, which is an actual tab in the user profile. In this library, the users listening history is visible and available to go through based on the amount the user listened to an artist/album/song, and in what time frame, but for example a simple search for a name within the library seems not possible, presenting a problem if a user only remembers partial information of an artist. Also, there is no grouping of artists in playlists or otherwise to create certain collections.
Figure 12 last.fm Song "Heart" Control (Highlighted)
Ease of use: With the many different categories and topics that can be
explored, the interface and software system require learning effort from the user. It provides many different channels and sources of information that sometimes intrude on the actual task with some of the information seemingly being more for the sake of the information than for the users benefit.
Transparency: Last.fm often notes on the interface that users are free to
add information and make edits to the content. This can range from writing the description of an artist or album to adding tags defining the genre. While this favors adaptability of the system, relevant for the next point, this also shows the source of some of the data. At the same time, the user experience is often lead by choices on numerical data and statistics- like to show the popularity of artists songs in a timeframe chosen by the user and this information is usually provided, making it understandable how rankings and other metrics came to be.
Adaptability: The aforementioned aspects of visible and dynamic
numerical data and user input possibilities are part of what users can do to influence the system and shown information. The “home” page, Figure 10, for example is purely dynamic content that is generated based on the users listening behavior, similarly many other recommendation sections within the app use this. At the same time, last.fm has social features that
lets users comment on artists and albums, as well as editing information directly. This leads to a change in the pages character depending on what users do or write. Although thanks to the page layout, this is kept in check and information architecture-wise has a secondary position.
Effort: While last.fm certainly presents opportunities to discover a new
artist on almost every aspect of their application, the user is left with the feeling that quite some effort is needed to go through the different sources of the information, listen to different artists and make choices. There doesn´t seem to be some funnel functionality that helps to weed through all the information presented, which can feel overwhelming.
Feedback Collection
Last.fm´s most obvious mechanism is tracking the actual songs that user play. Every song is recorded as a “scrobble” in your profile. Since some users are very conscious about their statistics, there are actually third-party programs to help you add other music to your last.fm profile, because the homepage itself doesn’t allow that. In addition to “scrobbling” a song, the only other positive feedback is to “heart” a song, as mentioned before and shown in Figure 12, the press of the button signifies that you like the song especially. Negative feedback is simply recorded by skipping a song and not possible otherwise.
2.1.5 Allmusic
While services like Spotify, Pandora and also last.fm have a mass appeal and are used by people in many different countries, markets and groups, it needed an interviewee that is considerably more engaged with music discovery than the average listener to become aware of Allmusic – even though the service is actually the oldest one of the four here
discussed. It was founded in 1991 and originally relied on an actual book filled with music references and a CD with the equivalent digital information.
Founded and run by extremely passionate people, Allmusic managed to create a database with over 1400 genres, aggregating music as well as the information about their circumstances and achieved critical claim to be a cornerstone of tracking the development of modern music with their breadth. Allmusic didn´t just track other people’s work but actively contributed in publishing biographies and information about created music.
Features
Allmusic combines properties of a music magazine with an expert catalog of music and related information. Users of the service can create a profile and start adding albums to their profile which then is used by the service to provide you with personalized recommendation. They can also search using all 1400 and more genre names, specify release dates, and filter by rating.
The ratings are one major part that sets the before discussed services apart. Allmusic has adapted a classic 5-star rating method, where users can rate each album but not songs or artists individually.
The general interface provides the user with the option to explore new releases, often accompanied by an actual review, a curated Discover section, articles, personalized recommendations and access to the user’s profile as well as search function.
In the artist overview, the user is presented with a variety of information about the artist or album ranging from an overview of the
