Recommendation systems make suggestions about artifacts to a user. For instance, they may predict whether a user would be interested in seeing a particular movie. Social recomendation methods collect ratings of artifacts from many individuals and use nearest-neighbor techniques to make recommendations to a user concerning new artifacts. However, these methods do not use the significant amount of other information that is often available about the nature of each artifact --- such as cast lists or movie reviews, for example. This paper presents an inductive learning approach to recommendation that is able to use both ratings information and other forms of information about each artifact in predicting user preferences. We show that our method outperforms an existing social-filtering method in the domain of movie recommendations on a dataset of more than 45,000 movie ratings collected from a community of over 250 users. Introduction Recommendations are a part of everyday life. We usually...

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Recommender systems improve access to relevant products and information by making personalized suggestions based on previous examples of a user's likes and dislikes. Most existing recommender systems use collaborative filtering methods that base recommendations on other users' preferences. By contrast, content-based methods use information about an item itself to make suggestions. This approach has the advantage of being able to recommend previously unrated items to users with unique interests and to provide explanations for its recommendations. We describe a content-based book recommending system that utilizes information extraction and a machine-learning algorithm for text categorization. Initial experimental results demonstrate that this approach can produce accurate recommendations.

The explosive growth of mailing lists, Web sites and Usenet news demands effective filtering solutions. Collaborative filtering combines the informed opinions of humans to make personalized, accurate predictions. Content-based filtering uses the speed of computers to make complete, fast predictions. In this work, we present a new filtering approach that combines the coverage and speed of content-filters with the depth of collaborative filtering. We apply our research approach to an online newspaper, an as yet untapped opportunity for filters useful to the wide-spread news reading populace. We present the design of our filtering system and describe the results from preliminary experiments that suggest merits to our approach.

The proliferation of web-based social networks has lead to new innovations in social networking, particularly by allowing users to describe their relationships beyond a basic connection. In this dissertation, I look specifically at trust in web-based social networks, how it can be computed, and how it can be used in applications. I begin with a definition of trust and a description of several properties that affect how it is used in algorithms. This is complemented by a survey of web-based social networks to gain an understanding of their scope, the types of relationship information available, and the current state of trust. The computational problem of trust is to determine how much one person in the network should trust another person to whom they are not connected. I present two sets of algorithms for calculating these trust inferences: one for networks with binary trust ratings, and one for continuous ratings. For each rating scheme, the algorithms are built upon the defined notions of trust. Each is then analyzed theoretically and with respect to simulated and actual trust networks to determine how accurately they calculate the opinions of people in the system. I show that in both rating schemes the algorithms

The aim of this article is to present and discuss selected commercial user modeling systems against the background of deployment requirements in real-world environments. Following the recent trend towards personalization on the World Wide Web, these systems are mainly aimed at supporting e-commerce including customer relationship management. In order to guide and structure our review, we dene a requirements catalogue that comprises the main dimensions of functionality, data acquisition, representation, flextensibility and flexibility, integration of external user-related information, compliance with standards, concern for privacy, and system architecture. Apart from the novelty of such a comparison both inside and outside the classical user modeling literature, a presentation of the core features of these commercial systems may provide a source of information and inspiration for the design, implementation, and deployment of future user modeling systems in research and commercial environments.

Abstract. Empirical evaluations are needed to determine which users are helped or hindered by user-adapted interaction in user modeling systems. A review of past UMUAI articles reveals insuf¢cient empirical evaluations, but an encouraging upward trend. Rules of thumb for experimental design, useful tests for covariates, and common threats to experimental validity are presented. Reporting standards including effect size and power are proposed. Key words: empirical evaluation, experimental design, covariant variables, e¡ect size, treatment magnitude, power, sensitivity. 1. What Is Empirical Evaluation? Empirical evaluation refers to the appraisal of a theory by observation in experiments. The key to good empirical evaluation is the proper design and execution of the experiments so that the particular factors to be tested can be easily separated from other confounding factors. For example, one may want to test whether a software system with a user model works better than the same system without a user model, test the effect of different levels of user modeling or different user model parameter settings, or test different user interfaces. These factors, which

. We describe the user modeling and personalization techniques adopted in SETA, a prototype toolkit for the construction of adaptive Web stores which customize the interaction with users. The Web stores created using SETA suggest the items best fitting the customers' needs and adapt the layout and the description of the store catalog to their preferences and expertise. SETA uses stereotypical information to handle the user models and applies personalization rules to dynamically generate the hypertextual pages presenting products. The system adapts the graphical aspect, length and terminology used in the descriptions to parameters like the user's receptivity, expertise and interests. Moreover, it maintains a model associated with each person the goods are selected for; in this way, multiple criteria can be applied for tailoring the selection of items to the preferences of their beneficiaries. Keywords: user modeling, personalized information presentation, customization of Web stores, ...

We describe several Markov models derived from the behaviour patterns of many users, which predict which documents a user is likely to request next. We then present comparative results of the predictive accuracy of the different models, and, based on these results, build hybrid models which combine the individual models in different ways. These hybrid models generally have a greater predictive accuracy than the individual models. The best models will be incorporated in a system for pre-sending WWW documents.

Abstract. We describe the user modeling and personalization techniques adopted in SETA, a shell supporting the construction of adaptive Web stores which customize the interactions with users, suggesting the items best fitting their needs, and adapting the description of the store catalog to their preferences and expertise. SETA uses stereotypical information to handle the user models and applies personalization rules to dynamically generate the hypertextual pages presenting products: the system adapts the graphical aspect, length and terminology used in the descriptions to the user’s receptivity, expertise and interests. Moreover, it maintains a profile associated to each person the goods are selected for, to provide multiple criteria for the selection of items, tailored to the beneficiaries ’ preferences. 1