Context-aware movie recommendations: An empirical comparison of pre-filtering, post-filtering and contextual modeling approaches
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Springer Berlin HeidelbergDate
2013Citation
10.1007/978-3-642-39878-0_13
E-Commerce and Web Technologies: 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013. Proceedings. Lecture Notes in Business Information Processing, Volumen 152. Springer, 2013. 137-149.
ISSN
1865-1348ISBN
978-3-642-39877-3 (print); 978-3-642-39878-0 (online)DOI
10.1007/978-3-642-39878-0_13Funded by
This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542)Project
Comunidad de Madrid. S2009/TIC-1542/MA2VICMREditor's Version
http://dx.doi.org/10.1007/978-3-642-39878-0_13Subjects
Context-aware recommender systems; Contextual modeling; Post-filtering; Pre-filtering; Social context; Time context; InformáticaNote
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39878-0_13Proceedings of 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013.
Rights
© Springer-Verlag Berlin Heidelberg 2013Abstract
Context-aware recommender systems have been proven to improve the performance of recommendations in a wide array of domains and applications. Despite individual improvements, little work has been done on comparing different approaches, in order to determine which of them outperform the others, and under what circumstances. In this paper we address this issue by conducting an empirical comparison of several pre-filtering, post-filtering and contextual modeling approaches on the movie recommendation domain. To acquire confident contextual information, we performed a user study where participants were asked to rate movies, stating the time and social companion with which they preferred to watch the rated movies. The results of our evaluation show that there is neither a clear superior contextualization approach nor an always best contextual signal, and that achieved improvements depend on the recommendation algorithm used together with each contextualization approach. Nonetheless, we conclude with a number of cues and advices about which particular combinations of contextualization approaches and recommendation algorithms could be better suited for the movie recommendation domain.
Files in this item
Google Scholar:Campos Soto, Pedro G.
-
Fernández-Tobías, Ignacio
-
Cantador Gutiérrez, Iván
-
Díez Rubio, Fernando
This item appears in the following Collection(s)
Related items
Showing items related by title, author, creator and subject.