Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches

Campos P.G.; Fernandez-Tobias I.; Cantador I.; Diez F.

Abstract

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, postfiltering 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. © Springer-Verlag Berlin Heidelberg 2013.

Más información

Título según WOS: Context-Aware Movie Recommendations: An Empirical Comparison of Pre-filtering, Post-filtering and Contextual Modeling Approaches
Título según SCOPUS: Context-aware movie recommendations: An empirical comparison of pre-filtering, post-filtering and contextual modeling approaches
Título de la Revista: DECISION SUPPORT SYSTEMS VI - ADDRESSING SUSTAINABILITY AND SOCIETAL CHALLENGES
Volumen: 152
Editorial: SPRINGER-VERLAG BERLIN
Fecha de publicación: 2013
Página de inicio: 137
Página final: 149
Idioma: English
Notas: ISI, SCOPUS