Personalized navigation of heterogeneous product spaces using SmartClient

Personalization in e-commerce has so far been server-centric, requiring users to create a separate individual profile on each server that they like to access. As product information is increasingly coming from multiple and heterogeneous sources, the number of profiles becomes unmanageably large. We present SmartClient, a technology based on constraint programming where a thin but intelligent client provides personalized information access for its user. As the process can run on the user's side, it allows much stronger filtering and visualization support with a wider range of personalization options than existing tools. It also eliminates the need to personalize many sites individually with different parameters, and supports product configuration and integration of different information sources in the same framework. We illustrate the technology using an application in travel e-commerce, which is currently under commercial deployment

Agile preference models based on soft constraints

An accurate model of the user’s preferences is a crucial element of most decision support systems. It is often assumed that users have a well-defined and stable set of preferences that can be elicited through a set of questions. However, recent research has shown that people very often construct their preferences on the fly depending on the available decision options. Thus, their answers to a series of questions before seeing decision options are likely to be inconsistent and often lead to erroneous models. To accurately capture preference expressions as people make them, it is necessary for the preference model to be agile: it should allow decision making with an incomplete preference model, and it should let users add, retract or revise individual preferences easily. We show how constraint satisfaction and in particular soft constraints provide the right formalism to do this, and give examples of its implementation in a travel planning tool

Scalable intelligent electronic catalogs

The world today is full of information systems which make huge quantities of information available. This incredible amount of information is clearly overwhelming Internet endusers. As a consequence, intelligent tools to identify worthwhile information are needed, in order to fully assist people in finding the right information. Moreover, most systems are ultimately used, not just to provide information, but also to solve problems. Encouraged by the growing popular success of Internet and the enormous business potential of electronic commerce, e-catalogs have been consolidated as one of the most relevant types of information systems. Nearly all currently available electronic catalogs are offering tools for extracting product information based on key-attribute filtering methods. The most advanced electronic catalogs are implemented as recommender systems using collaborative filtering techniques. This dissertation focuses on strategies for coping with the difficulty of building intelligent catalogs which fully support the user in his purchase decision-making process, while maintaining the scalability of the whole system. The contributions of this thesis lie on a mixed-initiative system which is inspired by observations on traditional commerce activities. Such a conversational model consists basically of a dialog between the customer and the system, where the user criticizes proposed products and the catalog suggests new products accordingly. Constraint satisfaction techniques are analyzed in order to provide a uniform framework for modeling electronic catalogs for configurable products. Within the same framework, user preferences and optimization constraints are also easily modeled. Searching strategies for proposing the adequate products according to criteria are described in detail. Another dimension of this dissertation faces the problem of scalability, i.e., the problem of supporting hundreds, or thousands of users simultaneously using intelligent electronic catalogs. Traditional wisdom would presume that in order to provide full assistance to users in complex tasks, the business logic of the system must be complex, thus preventing scalability. SmartClient is a software architectural model that uses constraint satisfaction problems for representing solution spaces, instead of traditional models which represent solution spaces by collections of single solutions. This main idea is supported by the fact that constraint solvers are extreme in their compactness and simplicity, while providing sophisticated business logic. Different SmartClient architecture configurations are provided for different uses and architectural requirements. In order to illustrate the use of constraint satisfaction techniques for complex electronic catalogs with the SmartClient architecture, a commercial Internet-based application for travel planning, called reality, has been successfully developed. Travel planning is a particularly appropriate domain for validating the results of this research, since travel information is dynamic, travel planning problems are combinatorial, and moreover, complex user preferences and optimization constraints must be taken into consideration

Agile preference models based on soft constraints

An accurate model of the user's preferences is a crucial element of most decision support systems. It is often assumed that users have a well-defined and stable set of preferences that can be elicited through a set of questions. However, recent research has shown that people very often construct their preferences on the fly depending on the available decision options. Thus, their answers to a series of questions before seeing decision options are likely to be inconsistent and often lead to erroneous models. To accurately capture preference expressions as people make them, it is necessary for the preference model to be agile: it should allow decision making with an incomplete preference model, and it should let users add, retract or revise individual preferences easily. We show how constraint satisfaction and in particular soft constraints provide the right formalism to do this, and give examples of its implementation in a travel planning tool. Copyright © 2005, American Association for Artificial Intelligence (www.aaai.org). All rights reserved

Determinants of online leisure travel planning decision processes :a segmented approach

D.B.A. ThesisThere is an abundance of information sources on the Internet that consumers use to plan and book their travel. This information reflects the fact that travel comprises a significant part of the business conducted through the web. Consumers are sometimes faced with a complex task of making purchasing decisions in the dynamic and fast-paced medium of the Internet. In spite of the importance of travel and the intricacies of the decision process, an integrated framework that identifies the various determinants of the online leisure travel planning decision process and how they interact, is largely absent in travel literature. This study aims to make a contribution by extracting from relevant literature useful elements that could comprise such a framework. It also uses several phases of qualitative research to refine the framework, and then a quantitative assessment of data collected from an online questionnaire completed by 1,198 respondents to test specific components of the framework that deal with online travel booking intention. In the final model building stage, three logistic regression models were compared. The first is a parsimonious one containing key determinants that lead to online travel booking intention. These determinants emerged from theoretical frameworks of the theory of reasoned action and innovation adoption theory. The second Model used strictly involvement, motivation, and knowledge variables that are thought to influence online booking intention. The third Model included a combination of relevant predictor variables from the other two Models. The relationship between various demographics and online travel booking intention was investigated yielding some interesting insights. Consequently, this study recommends these demographic variables be considered in segmenting travelers to find those more likely to book online. The determinants of online leisure travel booking decision processes could be used in conjunction with demographic variables to more accurately predict leisure travel website usage

Design and evaluation issues for user-centric online product search

Nowadays more and more people are looking for products online, and a massive amount of products are being sold through e-commerce systems. It is crucial to develop effective online product search tools to assist users to find their desired products and to make sound purchase decisions. Currently, most existing online product search tools are not very effective in helping users because they ignore the fact that users only have limited knowledge and computational capacity to process the product information. For example, a search tool may ask users to fill in a form with too many detailed questions, and the search results may either be too minimal or too vast to consider. Such system-centric designs of online product search tools may cause some serious problems to end-users. Most of the time users are unable to state all their preferences at one time, so the search results may not be very accurate. In addition, users can either be impatient to view too much product information, or feel lost when no product appears in the search results during the interaction process. User-centric online product search tools can be developed to solve these problems and to help users make buying decisions effectively. The search tool should have the ability to recommend suitable products to meet the user's various preferences. In addition, it should help the user navigate the product space and reach the final target product without too much effort. Furthermore, according to behavior decision theory, users are likely to construct their preferences during the decision process, so the tool should be designed in an interactive way to elicit users' preferences gradually. Moreover, it should be decision supportive for users to make accurate purchasing decisions even if they don't have detail domain knowledge of the specific products. To develop effective user-centric online product search tools, one important task is to evaluate their performance so that system designers can obtain prompt feedback. Another crucial task is to design new algorithms and new user interfaces of the tools so that they can help users find the desired products more efficiently. In this thesis, we first consider the evaluation issue by developing a simulation environment to analyze the performance of generic product search tools. Compared to earlier evaluation methods that are mainly based on real-user studies, this simulation environment is faster and less expensive. Then we implement the CritiqueShop system, an online product search tool based on the well-known critiquing technique with two aspects of novelties: a user-centric compound critiquing generation algorithm which generates search results efficiently, and a visual user interface for enhancing user's satisfaction degree. Both the algorithm and the user interface are validated by large-scale comparative real-user studies. Moreover, the collaborative filtering approach is widely used to help people find low-risk products in domains such as movies or books. Here we further propose a recursive collaborative filtering approach that is able to generate search results more accurately without requiring additional effort from the users

Advances in Online Shopping Interfaces: Product Catalog Maps and Recommender Systems

Over the past two decades the internet has rapidly become an important medium to retrieve information, maintain social contacts, and to do online shopping. The latter has some important advantages over traditional shopping. Products are often cheaper on the internet, internet companies sell a wider collection of products and consumers can buy items whenever they like without leaving their homes. On the other hand, the current state of online shops still has two major disadvantages over `real' shops: Products are often much harder to find than in traditional shops and there are no salesmen to advise the customers. In this thesis, we address both these disadvantages. We introduce and evaluate several new user interfaces for online shops that are based on representing products in maps instead of lists to user, such that products are easier to find. In these maps similar products are located close to each other. To create these maps, statistical techniques such as multidimensional scaling are used. Furthermore, we combine these maps with recommender systems to address the second disadvantage and to help the user in finding the product best suiting her needs. Also, we introduce a recommender system that is able to explain the recommendations it gives to users. We think that the methods discussed in this thesis can form a basis for new promising online shopping interfaces both in research as in practice

Improving user confidence in decision support systems for electronic catalogs

Decision support systems for electronic catalogs assist users in making the right decision from a set of possible choices. Common examples of decision making include shopping, deciding where to go for holidays, or deciding your vote in an election. Current research in the field is mainly focused on improving such systems in terms of decision accuracy, i.e. the ratio of correct decisions out of the total number of decisions taken. However, it has been widely recognized recently that another important dimension to consider is how to improve decision confidence, i.e. the certainty of the decision maker that she has made the best decision. We first review multi-attribute decision theory –the underlying framework for electronic catalogs– and present the state-of-the-art research in e-catalogs. We then describe objective and subjective measures to evaluate such systems, and propose a system baseline for achieving more accurate and meaningful comparative evaluations. We propose a framework to study the building of decision confidence within the query-feedback search interaction model, and use it to compare different types of system feedback proposed in the literature. We argue that different types of system feedback based on constraints (e.g. conflict and corrective feedback), even if not novel as such, can be combined in order to improve decision confidence. This claim is further validated by simulations and experimental evaluation comparing constraint-based feedback to ranked list feedback