Abstract
Increasing popularity of event-based social networks (EBSNs) calls for the developments in event recommendation techniques. However, events are uniquely different from conventional recommended items because every event to be recommended is a new item. Traditional recommendation methods such as collaborative filtering techniques, which rely on users’ rating histories, are not suitable for this problem. In this paper, we propose a novel context-enhanced event recommendation method, which exploits the rich context in EBSNs by unifying content, social and geographical information. Experiments on a real-world dataset show promising results of the proposed method.
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Wang, Z., He, P., Shou, L., Chen, K., Wu, S., Chen, G. (2015). Toward the New Item Problem: Context-Enhanced Event Recommendation in Event-Based Social Networks. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_36
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DOI: https://doi.org/10.1007/978-3-319-16354-3_36
Publisher Name: Springer, Cham
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