L. Ardissono, C. Barbero, A. Goy, G. Petrone
Dip. di Informatica, University of Torino Corso Svizzera 185 10149 Torino, Italy
Phone: +39 - 011 - 7429111
E-mail:
fliliana, cris, goy, giovanna
g@di.unito.it
1. Introduction
The undi erentiated approach to mass marketing has been recently replaced with market segmentation techniques: psy- chographic and socio-demographic analyses are exploited to identify homogeneous market segments to which suitable products and services must be o ered ?].
At the same time, the advent of the electronic com- merce, especially in the customer-to-business perspective, has opened new opportunities for sales, as well as new is- sues to address in the design of on-line stores. In particular, since these stores are accessed by heterogeneous users, they should satisfy di erent needs and preferences in the selection of goods this ability requires ltering capabilities, to iden- tify the items most suited to the specic customers ?,?].
Moreover, since Web stores are hypermedia systems, they should meet the users' needs in what concerns the interac- tion style. As discussed in ?], users di er with respect to many parameters (e.g. their status, expertise, preferences and the reason for connecting to the system), which should be taken into account in order to enhance the usability of systems.
Traditionally, the research on electronic sales has mainly focused on order processing and secure payment transac- tions ?]. Only recently the personalization of the inter- actions with customers has received special attention (e.g., see ?,?,?]). The customization of a virtual store involves many tasks: these range from a detailed characterization of product features, which is essential to support a exible selection of items suiting the customers' needs, to the iden- tication of the special features of the customer groups to which the store products are directed.1 In particular, the relevant products should be selected for presentation, and their description should be tailored to the user's expertise
1The knowledge about customer preferences is fundamental to the personalization task and must be accurately de ned, after having an- alyzed the features of the market segments to which the store is di- rected ?].
and interests, in order to enable her to evaluate items more accurately than just looking at a raw product catalog. This aspect in uences the selection of the information to present and its linguistic (or pictorial) form e.g. the descriptions might use more or less technical terms images could be added to improve the comprehension the product descrip- tions should be focused on the features relevant to the users (e.g., see ?,?,?, ?]). Customers could even be helped by virtual sales assistants, who guide them in their browsing and product selection activity (e.g. ?,?]).
Various techniques have been used to select interesting items in environments where heterogeneous information sources are exploited, or little information is available about the user's needs for instance, see ?,?,?,?,?]. We believe that, while those techniques are suited to deal with large- scale applications, such as information retrieval on the Web, virtual stores can benet from at least two main factors:
The descriptions of products are stored in databases.
So, the products are described in a structured way this aspect has been exploited in the automatic generation of some virtual catalogs, like ?] and ?]. Moreover, the search task is performed in the presence of signi- cant information about the structure of the hyperspace nodes.
We assume that, at least in domains where medium complexity items are sold, many users need help from the system when choosing the items to purchase so, they are willing to cooperate with the system and pro- vide information about themselves (and their needs), if this is useful for the system to guide them in their selections.
For these reasons, we have adopted a knowledge-intensive approach, which enables us to obtain detailed user proles and select items and descriptions on the basis of a deep eval- uation of the user's needs. Of course, in the development of our system, we have paied special attention to the issue of obtaining a virtual storeshell, which can be instantiated on several sales domains to produce di erent Web stores. To this purpose, the whole domain-dependent knowledge about users and products (user features and product properties) is stored in declarative knowledge bases, which can be cong- ured by means of specialized (graphical) tools.
UMC
Product Extractor
Users DB
Products DB UM
Personalization Agent
Dialog Manager
Shopping Cart Manager Product Dialog
Tax view Context
Session Manager
W E B S E R V E R Users
DB Manager
Products DB Manager
insert ask-one ask-all
tell(1)
ask-one
ask-one
tell insert
tell
tell
tell(2)
tell ask-one tell
tell
ask-one tell
tell tell
post
post
Shopping Cart Stereotype
KB
Product Taxonomy
delete
Figure 1: The Web store architecture.
2. Our work
Our project has the goal to develop a Web store architec- ture supporting a personalized interaction with users. Our system includes a (customizable) store shell and the cong- uration tools to set up a new Web store ?, ?,?, ?]. We have dened a multiagent architecture where various agents collaborate to the personalization of the interactions with the user (see ?] for more details).
Figure 1 shows the architecture of our shell: the rectan- gles represent agents, while the ovals represent the Users and Products databases (DBs), containing information about the users who visited the store and the items sold there.
The parallelograms represent the knowledge bases (contain- ing a conceptual representation of user classes and prod- uct types) and the dynamic structures maintained during each working session. The solid arrows show the informa- tion ow among agents. The dotted arrows show the data ow between agents and information structures e.g. the User Modeling Component (UMC) retrieves the stereotypes from the Stereotype KB. The dashed lines relate knowledge bases with the data structures initialized on the basis of their contents (i.e. the user models - UMi- and the Product Tax- onomy View, representing the personalized hyperspace).
In the architecture, the Session Manager is the interface between the store and the Web: it receives messages from the user's browser and forwards to it the hypertextual pages to be displayed. Moreover, it manages a multi-user access to the virtual store. The Dialog Manager handles the interac- tion with the user and keeps the dialog context. The Shop- ping Cart Manager handles the user's shopping cart. The Products and Users DB Managers handle, respectively, the Users and Products databases. The User Modeling Compo- nent, Product Extractor and the Personalization Agent are described in the following.
2.1 The UMC
The User Model is a dynamic structure holding the infor- mation about a user. In a working session, the UMC keeps a model for the direct user and a model for each beneciary of the selected products: the direct user model is used to tailor the presentation of items the other models are used to select the goods suited to the person they are directed to.
Each user model contains a descriptive part where the infor- mation requested to the user is stored: personal data (age, gender, job, education level), main functionalities needed by the user (inferred from her requests for product types)
moreover, it contains a predictive part where the system at- tributes the user some personality traits and preferences.
The personality traits (e.g. receptivity,2 domain knowledge, technical interest) take linguistic values (low, medium, high) and are a synthetic description of the direct user's presenta- tion preferences and needs this description is used to apply the personalization rules which select the information and the description style most suited to her.
The preferences, instead, represent the user's attitudes to- ward product properties (e.g. toward their ease of use) and are used in order to select the items most suited to her needs.
Each preference is represented by means of a parameter, structured as follows:3
Parameter Name Importance
Values: <Linguistic Value, Probability>pairs
The Importance slot represents the system's belief about how much the property is considered important by the user
it takes values in the range 0..1].4
Each<Linguistic Value, Probability> pair contains:
2I.e. capability to acquire large amounts of information.
3The representation of preferences is derived from the representa- tion introduced in ?].
4For simplicity, we did not attach probabilities to the importance slot.
Figure 2: A product presentation page dynamically generated by our system.
- a linguistic value that the parameter can assume
- the probability that the user prefers that linguistic value for the product property represented by the parameter.
For example, a preference towards the products \ease of use"
could be described as follows:
Ease of use:
Importance: 1
Values:<Low, 0>,<Medium, 0.3>,<High, 0.7>
This means that the user considers the ease of use an im- portant property moreover, she very likely prefers easy to use products (with a probability of 0.7), she prefers average complexity products with probability 0.3, while she certainly does not like complex products.
When a customer accesses the virtual store, her user model is initialized either by loading the corresponding record stored in the User's DB (if there is one), or by exploiting stereotypical information, that is stored in the Stereotype KB.The Stereotype KB contains a hierarchical taxonomy of stereotypes clustering properties of customer groups. For our prototype, we dened the stereotypes by adapting to the telecommunication domain a set of data collected in psycho- graphic studies on the Italian population.
2.2 The Product Extractor
The Product Extractor selects the items which should be suggested to the user: after the Products DB Manager re- trieves the records of the items matching the user's queries from the Products DB (e.g. the phone models available in the shop), the Product Extractor rates the items and sorts them, depending on how close they match the preferences
stored in their beneciary's model. In this way, the items can be presented to the user showing the most appropriate ones rst.
The matching between the beneciary's preferences and the item properties is possible because the records in the Products DB contain the items' features andproperties(e.g.
their quality and ease of use). These properties coincide with the preferences towards product properties specied in the user models. In particular, the item properties are represented by reporting, for each of them, the linguistic value which suits the item. For instance, the \Vivavoce T200" integrated phone is described as follows:
Features:
code: VivaVoce T200
price: LIT. 90.000
agenda: 10 numbers
<other features ...>
Properties:
quality: high
ease of use: medium
<other properties ...>
Given a set of retrieved items, the Product Extractor gets from the UMC the preferences of the person to which the goods are directed (their beneciary). Then, it rates each item by evaluating how close its properties match the ben- eciary's preferences. An item suited to a person should match all her relevant preferences, possibly ignoring irrel- evant mismatching properties so, the beneciary's prefer- ences are used as classication conditions for evaluating the overall matching degree of the item and are combined in a formula implementing a fuzzy AND. In this \classication"
phase, the impact of each property is ltered depending on
Figure 3: Description tailored to a non-expert user.
how important is the property (\Importance" eld of the preference in the beneciary's user model): in this way, ir- relevant mismatching properties are ignored.
2.3 The Personalization Agent
This agent dynamically generates the hypertextual pages de- scribing the store catalog. It tailors the content and layout of these pages to the user's characteristics: at each stage, it selects the specic pieces of information to show and the lin- guistic form to adopt. The Personalization Agent gets from the UMC the data needed to apply a set of personalization rules (see ?] for details) to decide which product features should be described (because they are assumed to be most relevant to the user) and which linguistic form should be selected. Three descriptions types for each product feature are available in the Personalization Agent's knowledge base
these descriptions correspond to three degrees of complex- ity and technicality (low, medium, high): the low-level de- scriptions are generally longer and use simpler words, while the high-level ones are concise and use a technical language.
The rules in uence the linguistic form of descriptions, but also the number and the order in which the features are de- scribed: the number of features to display is dened on the basis of the user's receptivity, while their order is decided on the basis of her interests for instance, if the user is more in- terested in aesthetic features, these ones are displayed rst.
The result of the application of the personalization rules is that di erent pages can be produced to describe the same products and items. For example, compare Figures 2 and 3:
the rst one shows a page describing the \Super Slim" phone to an expert user, who is supposed to easily understand detailed and technical descriptions. Figure 3 focuses on the description of the same item, but it has been produced for a non-expert user as it can be noticed, this page shows fewer features moreover, the sentences displayed are simpler and more intuitive than those in Figure 2.
Given the items to be displayed, the Personalization Agent selects and sorts the features to be described on the basis of their relevance to the user's preferences, receptivity and domain expertise.
As shown in Figure 2, the hypertextual pages are split into several areas:
In the leftmost portion of the page the system displays the user's selections and enables her to switch among
the active dialog contexts. For each selected product, a block of the vertical bar species which product the user has initially selected for inspection (\Initial se- lection"), which information has been most recently shown (\Last visited"), who is the beneciary of the good (\for yourself", \for Paul"), and to which use the product is addressed (home vs. business, denoted by means of icons). The product currently displayed is highlighted.
The area describing the functionalities and features of- fered by the products is tailored to the user's interests and expertise level, so that more or less detailed and technical descriptions are produced, depending on her domain expertise more or less compact pages are pro- vided according to her receptivity, and so forth. Below this area, there is the bar containing the links to the more specic/generic products in the Product Taxon- omy View (\GO TO", \BACK TO").
The topmost bar provides the links to the main prod- uct categories available in the store, while the bar lo- cated at the bottom of the page contains the general control buttons (e.g. the \EXIT" button, the link to the map of the site, and so on).
3. Conclusions
We have described an adaptive Web store architecture o er- ing personalized interactions to customers, on the basis of their expertise on the domain, preferences and needs. The system dynamically generates the hypertextual pages de- scribing the virtual catalog. It selects the items which best match the user's preferences and tailors their presentation to the users' characteristics, varying the content of the pages and the detail level of the descriptions accordingly. The system can be congured on di erent sales domains, so that multiple Web stores are obtained out of a single shell.
For the moment, we have tested our prototype and tuned it on the basis of the suggestions collected from a restricted number of users (including psychologists and computer sci- ence experts), who have helped us to improve the inter- face and the interaction mode. A eld trial is necessary to test the system with real users. As future work, we would like to increase the system's initiative: e.g., during an in- teraction, the system should interact more frequently with
the user, asking her whether she would prefer items with slightly di erent characteristics ?] moreover, the system should promptly react to the user's behavior, exploiting her interests to promote specic products ?]. We feel that re- active planning approaches, as that of ?], could be very e ective to this task.
Our system is a Three Tier Application Architecture written in Java and based on the JavaSoft Java Web Server 1.1 see ?,?] for details.
4. Acknowledgements
This work is developed in the project \Servizi Telematici Adattativi" (http://www.di.unito.it/~ seta), carried on at the Dipartimento di Informatica of the University of Torino within the national initiative \Cantieri Multimediali", granted by Telecom Italia. We are grateful to L. Console, L. Lesmo, C. Simone and P. Torasso for having contributed signicantly to this work with suggestions and fruitful dis- cussions.
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