Extending Content-Boosted Collaborative Filtering for Context-aware, Mobile Event Recommendations

Recommender systems support users in filtering large amounts of data to find interesting items like restaurants, movies or events. Recommending events poses a bigger challenge than recommending items of many other domains. Events are often unique and have an expiration date. Ratings are usually not available before the event date and not relevant after the event has taken place. Content-boosted Collaborative Filtering (CBCF) is a hybrid recommendation technique which promises better recommendations than a pure content-based or collaborative filtering approach. In this paper, CBCF is adapted to event recommendations and extended by context-aware recommendations. For evaluation purposes, this algorithm is implemented in a real working Android application we developed. The results of a two-week field study show that the algorithm delivers promising results. The recommendations are sufficiently diversified and users are happy about the fact that the system is context-aware. However, the study exposed that further event attributes should be considered as context factors in order to increase the quality of the recommendations.