Authors:
Daniel Herzog
and
Wolfgang Wörndl
Affiliation:
Technische Universität München, Germany
Keyword(s):
Recommender System, Event Recommendations, Content-Boosted Collaborative Filtering, Context-awareness, Mobile Application
Related
Ontology
Subjects/Areas/Topics:
Context-Awareness
;
Enterprise Information Systems
;
Mobile Information Systems
;
Recommendation Systems
;
Software Agents and Internet Computing
;
Web Information Systems and Technologies
Abstract:
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.
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