Résumé. Des travaux récents (Pilaszy et al., 2009) suggèrent que les métadonnées sont quasiment inutiles pour les systèmes de recommandation, y compris en situation de cold-start : les données de logs de notation sont beaucoup plus informatives. Nous étudions, sur une base de référence de logs d'usages pour la recommandation automatique de DVD (Netflix), les performances de systèmes de recomman...

News recommendation has been a must-have service for most mobile device users to know what has happened in the world. In this paper, we focus on recommending latest news articles to new users, which consists of the new user coldstart challenge and the new item (i.e., news article) coldstart challenge, and is thus termed as dual cold-start recommendation (DCSR). As a response, we propose a solut...

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trus...

Recommendation algorithms and multi-class classifiers can support users of social bookmarking systems in assigning tags to their bookmarks. Content based recommenders are the usual approach for facing the cold start problem, i. e., when a bookmark is uploaded for the first time and no information from other users can be exploited. In this paper, we evaluate several recommendation algorithms in ...

4 Implementation remarks 12 4.1 Calling sequence in direct communication . . . . . . . . 12 4.2 Calling sequence in reverse communication . . . . . . . . 13 4.3 More on some arguments . . . . . . . . . . . . . . . . . 15 4.4 More on some output modes . . . . . . . . . . . . . . . . 15 4.5 Cold start and warm restart . . . . . . . . . . . . . . . . 16 4.6 Usage for very large scale problems . . ...

Recommender systems suffer from the new user problem, i.e., the difficulty to make accurate predictions for users that have rated only few items. Moreover, they usually compute recommendations for items just in one domain, such as movies, music, or books. In this paper we deal with such a cold-start situation exploiting cross-domain recommendation techniques, i.e., we suggest items to a user in...

Modern recommender systems model people and items by discovering or ‘teasing apart’ the underlying dimensions that encode the properties of items and users’ preferences toward them. Critically, such dimensions are uncovered based on user feedback, often in implicit form (such as purchase histories, browsing logs, etc.); in addition, some recommender systems make use of side information, such as...

We study the cold-start link prediction problem where edges between vertices is unavailable by learning vertex-based similarity metrics. Existing metric learning methods for link prediction fail to consider communities which can be observed in many real-world social networks. Because di↵erent communities usually exhibit di↵erent intra-community homogeneities, learning a global similarity metric...

The cold start problem, frequent with recommender systems, addresses the issue in cases where we don’t know enough about our users (e.g., the user hasn’t rated anything yet, or there are no user activities) in that specific domain. In our paper we present a simple and robust transfer learning approach where we model users’ behavior in a source domain, transferring that knowledge to a new, targe...