Managing irrelevant contextual categories in a movie recommender system
MetadataShow full item record
Since the users' decision making depends on the situation the user is in, contextual information has shown to improve the recommendation procedure in context-aware recommender systems (RS). In our previous work we have shown that relevant contextual factors have significantly improved the quality of rating prediction in RS, while the irrelevant ones have degraded the prediction. In this work we focus on the detection of relevant contextual conditions (i.e., values of contextual factors) which influence the users' decision making process. The goals are (i) to lower the intrusion for the end user by simplifying the acquisition process, and (ii) to reduce the sparsity of the acquired data during the contextual modeling. The results showed significant improvement in the rating prediction task, when managing the irrelevant contextual conditions by the approach that we propose in this paper.
Showing items related by title, author, creator and subject.
Tkalcic M; Kosir A; Tasic J (CEUR-WS.org, 2011)Recommender systems have traditionally relied on data-centric descriptors for content and user modeling. In recent years we have witnessed an increasing number of attempts to use emotions in di?erent ways to improve the ...
Proceedings of the Workshop on Location-Aware Recommendations (LocalRec 2015) co-located with the 9th ACM Conference on Recommender Systems (RecSys 2015) Bouros, P; Lathia, N; Renz, M; Ricci, F; Sacharidis, D (CEUR, 2015)
Jannach D; Zanker M; Ge M; Gröning M (Springer Berlin Heidelberg, 2012)The paper reviews and classifies recent research in recommender systems both in the field of Computer Science and Information Systems. The goal of this work is to identify existing trends, open issues and possible directions ...