Armelle Brun

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Collaborative filtering-based recommender systems exploit user preferences about items to provide them with recommendations. These preferences are generally ratings. However, choosing a rating is no easy task for any user; the rating scale is usually reduced and the rating values given by the users may be influenced by many factors. The ratings are thus not(More)
Recommender systems provide users with pertinent resources according to their context and their profiles, by applying statistical and knowledge discovery techniques. This paper describes a new approach of generating suitable recommendations based on the active user's navigation stream, by considering long and short-distance resources in the history with a(More)
Recommender systems aim at suggesting to users items that fit their preferences. Collaborative filtering is one of the most popular approaches of recommender systems; it exploits users’ ratings to express preferences. Traditional approaches of collaborative filtering suffer from the cold-start problem: when a new item enters the system, it cannot be(More)
In this paper, a new topic identification method, WSIM, is investigated. It exploits the similarity between words and topics. This measure is a function of the similarity between words, based on the mutual information. The performance of WSIM is compared to the cache model and to the wellknown SVM classifier. Their behavior is also studied in terms of(More)
Neighborhood based collaborative filtering is a popular approach in recommendation systems. In this paper we propose to apply evolutionary computation to reduce the size of the model used for the recommendation. We formulate the problem of constructing the set of neighbors as an optimization problem that we tackle by stochastic local search. The results we(More)
Recommender systems contribute to the personalization of resources on the Web sites and information retrieval systems. In this paper, we present a hybrid recommender system using a user based approach which combines predictions based on Web usage patterns and rating data. We suggest a new technique that takes into account frequent patterns in order to(More)
As resource spaces become ever larger, the need for tools to help users find pertinent and reliable resources quickly and easily is more and more acute. Recommender systems are an efficient way to tackle the problem of information overload, as they enable to inference from users’ past behavior to suggest resources the users have not seen yet. Collaborative(More)