Context-aware movie recommendations: An empirical comparison of pre-filtering, post-filtering and contextual modeling approaches
EntityUAM. Departamento de Ingeniería Informática
PublisherSpringer Berlin Heidelberg
10.1007/978-3-642-39878-0_13E-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.
ISBN978-3-642-39877-3 (print); 978-3-642-39878-0 (online)
Funded byThis work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542)
ProjectComunidad de Madrid. S2009/TIC-1542/MA2VICMR
SubjectsContext-aware recommender systems; Contextual modeling; Post-filtering; Pre-filtering; Social context; Time context; Informática
NoteThe final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39878-0_13
Proceedings of 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013.
Rights© Springer-Verlag Berlin Heidelberg 2013
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.
Google Scholar:Campos Soto, Pedro G. - Fernández-Tobías, Ignacio - Cantador Gutiérrez, Iván - Díez Rubio, Fernando
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