Proceedings of the 2nd Workshop on Adaptive Systems and User Modeling on the WWW

A Temporary User Modeling Approach for Adaptive Shopping on the Web

Tanja Joerding
Multimedia Technology Group
Dresden University of Technology
01062 Dresden
Phone: 0049 351 4638117
Abstract. This work proposes a temporary user modeling approach that enables the immediate adaptation of product presentations to the individual customer at runtime by using an incremental learning algorithm. The paper introduces a first prototype system named TELLIM, describes the architecture, and focuses on future work.

1. Introduction

In the area of electronic shopping static product presentations on the Web cannot always meet the expectations of all customers. There are various hardware and software preconditions, customers have different preferences for multimedia elements, and they are interested in different information concerning the product.
First approaches of adaptive product catalogues with conventional user modeling techniques are not convincing [2], [3], [11]. Customers are not motivated to answer questions and they are often distrustful to give private data. Most of the user modeling techniques, like rule-based systems or collaborative filtering, are not flexible enough in that changing preferences of the customer are not taken into account. Another problem is the initialization of the system. For adaptive product presentations to become practical the companies need moduls that are easy to adapt to their own demands and products. This means that extended preparation of data related to the adaptation process should be unnecessary.
This work focuses on a temporary user modeling approach, that monitors the behavior of the customer and that realizes adaptive presentations without storing user data for other sessions. The customer can remain anonymous but uses a system that recognizes his needs and preferences and that adapts the product presentations immediately. Figure 1 shows in the center image how a car is presented in the beginning of a session. Depending on the user behavior, the next presentation is generated.

Figure 1: An example of an adaptive product presentation (full size)

If the user e.g. has chosen the text in the first presentation and has not enlarged the image or used the VR-world, the next presentation would be generated like in the left image. If the customer has ignored the textual links, but navigated in the virtual world and enlarged the image, the presentation of the next car would be like in the right image of Figure 1. This means that every piece of information concerning the product is visible for the customer, but preferred elements are presented in the foreground and uninteresting elements of the product description are presented only as links in the background.

2. Temporary user modeling

The development of temporary user models consists of three steps:

Monitoring interactions

To get information about the customer, we use only implicit knowledge acquisition by monitoring the behavior of the customer on the side of the client. These are interactions with single presentation elements, e.g. if the customer follows a certain link, starts audio or video players, interrupts the downloading of images, saves or prints an image or text, or takes a step in virtual reality worlds. In addition to such interactions, the system computes the time spent on a presentation element. If this time does not exceed a certain threshold, then this indicates that the element does not meet the preferences of the customer. Because of the permanent observation the system gets up-to-date information and it is unnecessary to annoy the customer with explicit questions.

Preprocessing data

Using a set of general rules, the system evaluates for every presentation element whether the customer was interested in it or not. These rules are quite simple. We extract them from our personal experience. In future, we have to evaluate them by experimental studies. Examples for such rules are: The evaluated presentation elements are then used as example data for an incremental learning algorithm.

Learning preferences

To learn the preferences of the customer, we use an incremental algorithm based on CDL4 [12] that considers attributes for every presentation element. In our first approach, we use the attributes:  type of medium, e.g. "audio", a set of content attributes, e.g. "{car, VW, GolfIV, engine}", and the downloading time, e.g. "3 sec". But, in first experiments, we faced the problem that rules became to specialized and that the processing of the set based attribute is quite complex. Therefore, to describe the content of the presentation element, we now use in our prototype three attributes: kind of product, e.g. "car", brand, e.g. "VW-Golf", and kind of information, e.g. "engine". In addition, we use no longer the downloading time as attribute, because during a session, the quality of the connection to the Internet is not changing very much. Instead, we consider only the size of the presentation element.
This means that example data for the algorithm looks like the following tuple: [audio, car, VW-Golf, engine, 100kB, positive]. The result of the algorithm is a decision list that can be interpreted as a list of rules. e.g. The chosen algorithm works incrementally, i.e. when the customer moves from one product presentation to another one, the algorithm receives the interaction data that are collected on a single page and updates its rule base. This means that we have a temporary view of the preferences of the customer at any time. If there is contradictory data, the algorithm prefers more recent data over older data. These properties are important because many customers navigate only for a short time in a catalogue. Additionally, while navigating in the product catalogue, the preferences of the customers can change. To satisfy their needs, the adaptation of the presentation should happen quickly and must be flexible.

3. The TELLIM System

At present, we have implemented a first prototype system named TELLIM (inTELLIgent Multimedia) and have applied the system to a small jumble sale for selling second-hand cars [7], [8], [9]. The presentation elements are integrated in a dynamic HTML (DHTML) page. They are implemented in different ways. Images and texts are realized as DHTML objects such that interactions with the customer can be monitored with event handlers of JavaScript. Audio, video and VR-worlds are integrated with Plug-Ins. These Plug-Ins have Java interfaces that facilitate the monitoring of customer interactions. The architecture of the system can be seen in Figure 2.

Figure 2: The architecture of the TELLIM system (full-size)

All interactions are registrated in a component on the client-side (Observer). This component is realized as a JavaApplet that is send to the customer's browser at the beginning of a session. It collects the observed interactions and then evaluates the presentation elements. Using Remote Method Invocation (RMI)1 [13], these evaluations are written in a central history list which is stored in a object-oriented database [5], [6] on the server-side. The information of the history list provides the example data for the learning algorithm.
The documents are generated at runtime by using dynamic templates. This means that for every product category, it exists a template that provides with help of "if-then-else" constructs different presentation alternatives. At runtime, the system asks the learning algorithm for a statement concerning every possible presentation element (Media). The algorithm decides with its current list of rules if the customer may be interested in the element or not. Therefore, the system integrates the elements in a presentation which are preferred by the customer, and adds other elements only as links.
At the beginning of a session, the system estimates with test signals the quality of the customer's connection to the Internet (Dt-predictor). Depending on this prediction, the system is initialized and generates the first product presentation with more or less multimedia elements.

4. Discussion and Future Work

Realizing the TELLIM system, we assume that the behavior of the customers can depend on the following parameters: The aim of the TELLIM system is to generate presentations that take the above mentioned situations into account. This means that the learned rules show not only general static preferences of the customers that are influenced e.g. by their technical preconditions, but also preferences that depends on the kind of product or the size of the presentation element. Additionally, the system considers whether preferences change and adapts the presentations immediately.
In a next step, we have to evaluate the implemented system. Based on ISO 9241-11 (Guidance on usability) and ISO 14598-1 (Evaluation of software quality) we want to study the following usability criteria [1]:

Effectiveness. Comparing to adaptive information systems that support the user in retrieving the right information from a large information space(e.g. [4]), in adaptive product catalogues the quality of the presentations is more important. Does the presentation show the expected information in a form that appeals to the preferences of the customer? To answer this question, we will analyze the context of use, e.g. What are the motivation of the user? What are the identifiable purposes? What are the technical preconditions? Then, we will compare the results with the behavior of the system.

Efficiency. Are adaptive product presentations more efficient for the customer? How much time and effort will be required to get information concerning the products? To answer these questions, we will monitor the number of within-page actions and the sum of downloading time. In the study of adaptive hypermedia, the task completion time is often used as a main evaluation criterion, e.g. [10]. In the application field of electronic shopping, the goal is to provide presentations that appeal to the customers and that meet their interests. The customers shall get high quality presentations such that they are enjoying the consuming of the presentations. It is not desirable that they leave the document as fast as possible. Therefore, we only want to know the time that is wasted e.g. by unnecessary interactions or by waiting for the downloading of presentation elements.

Satisfaction. Are the customers more satisfied? Are they enjoying the presentations? An important factor concerning the acceptance of adaptive product presentations is the subjective opinion of the customer. To get the subjects' own evaluation, we want to use questionnaires and interviews.
A first study will be done in a laboratory observing subjects in performing simulated task using systems with and without adaptivity. In future, we also planned to integrate the TELLIM system in a real product catalogue to study  customers in a real environment.

Additionally to an evaluation, we consider the following extensions of the system:

Refinement of the user modeling. As an extension of this approach, we want to refine the user modeling process. Therefore, we have to consider three phases:

Combination with a long-term user modeling. At present, we are concentrating on short-term user modeling so that every customer gets an individual presentation without thinking about data protection and privacy issues, because we do not store user data. For customers who visit the presentation regularly it might be interesting to combine the current system with optional long-term user modeling where more information can be stored and another kind of adaptation (e.g. suggestions for other products) can be realized.

5. References

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[2] Broadvision, Inc. (1998),

[3] Charles River Analytics Inc. (1998) Open Sesame,

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[5] Ingres Software (M): Jasmine (1998),

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[7] Joerding, T. & Meissner, K. (1998), Intelligent Multimedia Presentations in the Web: Fun without Annoyance, in: Proccedings of the Seventh International World Wide Web Conference (WWW7), Brisbane, Australia, pp. 649-650.

[8] Joerding, T., Michel, S., Popella, M. (1998), Intelligentes Marketing durch adaptive Produktpräsentationen im Web , in: M. Engelien & K. Bender (ed.) GeNeMe98 - Gemeinschaften in Neuen Medien , Josef Eul Verlag, pp. 279-292.

[9] Joerding, T. & Michel, S. (1999), Personalized Shopping in the Web by Monitoring the Customer, in: Proceedings of  "The Active Web - A British HCI Group Day Conference", Stafford, UK.

[10] Kaplan, C., Fenwick, J., Chen, J.  (1993), Adaptive Hypertext Navigation Based On User Goals and Context, in: User Modeling and User-Adapted Interaction Vol. 3 No. 3, pp. 193-220.

[11] Net Perceptions, Inc. (1998), Building Customer Loyalty and Profitable 1-to-1 Customer Relationship with Net Perceptionís GroupLensTM Recommendation Engine.

[12] Shen, W.M. (1996), An Efficient Algorithm for Incremental Learning of Decision Lists. Technical Report, USC-ISI-96-012, Information Sciences Institute, University of Southern California,

[13] Sun Microsystems, Inc. (1997), Remote Method Invocation Specification,

Communication mechanism. It is an API standard for building distributed Java systems.  In its current form, it uses its own proprietary architecture and transport layers.