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 . . ...

Generating personalized recommendations for new users is particularly challenging, because in this case, the recommender system has little or no user record of previously rated items. Connecting the newcomer to an underlying trust network among the users of the recommender system alleviates this socalled cold start problem. In this paper, we study the effect of guiding the new user through the ...

We employ universal schema for slot filling and cold start. In universal schema, we allow each surface pattern from raw text, and each type defined in ontology, i.e. TACKBP slots to represent relations. And we use matrix factorization to discover implications among surface patterns and target slots. First, we identify mentions of entities from the whole text corpus and extract relations between...

In this chapter we present a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations. Participants were requested to address the tasks by means of recommendatio...

This project develops a hybrid model that combines content-based with collaborative filtering (CF) for hotel recommendation. This model considers both hotel popularity in input destination and users preference. It produces the prediction with 53.6% accuracy on test data-4% improvement on purely content-based model. Addtionally, three issues are well-resolved when implementing CF: sparsity in ut...