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Recommender systems have proven to be an important response to the information overload problem, by providing users with more proactive and personalized information services. And collaborative filtering techniques have proven to be an vital component of many such recommender systems as they facilitate the generation of high-quality recom-mendations by(More)
The utility problem occurs when the cost associated with searching for relevant knowledge outweighs the benefit of applying this knowledge. One common machine learning strategy for coping with this problem ensures that stored knowledge is genuinely useful, deleting any structures that do not contribute to performance in a positive sense, and essentially(More)
Recently the world of the web has become more social and more real-time. Facebook and Twitter are perhaps the exemplars of a new generation of social, real-time web services and we believe these types of service provide a fertile ground for recommender systems research. In this paper we focus on one of the key features of the social web, namely the creation(More)
Recommender systems have traditionally recommended items to individual users, but there has recently been a proliferation of recommenders that address their recommendations to groups of users. The shift of focus from an individual to a group makes more of a difference than one might at first expect. This chapter discusses the most important new issues that(More)
Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and(More)
Critiquing is a powerful style of feedback for case-based recommender systems. Instead of providing detailed feature values, users indicate a directional preference for a feature. For example, a user might ask for a ‘less expensive’ restaurant in a restaurant recommender; ‘less expensive’ is a critique over the price feature. The value of critiquing is that(More)
Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the user’s feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a feature’s value-space. For example, a user may request a cheaper product, thus(More)
Case-based reasoning systems solve problems by reusing a corpus of previous problem solving experience stored as a case-base of individual problem solving cases. In this paper we describe a new technique for constructing compact competent case-bases. The technique is novel in its use of an explicit model of case competence. This allows cases to be selected(More)