Publication
CIKM 2016
Conference paper

Location-aware friend recommendation in event-based social networks: A Bayesian latent factor approach

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Abstract

In this paper we study the friend recommendation problem in event-based social networks (EBSNs). Effective friend recommendation is of benefit to EBSNs, since it can promote user interaction and accelerate information diffusion for promoted events. Different from usual friend recommendations, the aim of making friends in EBSNs is to better participate offline events and enhance user experience. Meanwhile friend recommendation in EBSNs encounters three types of data, i.e. geographical information, implicate user rating, and user behavior. These differences imply that existing friend recommendation approaches are not adequate any more for EBSNs. Under this background, in this paper we propose a Bayesian latent factor model, which can jointly formulate above three types of data, for friend recommendation with better event promotion and user experience. Results on real-world datasets show the efficacy of our approach.

Date

24 Oct 2016

Publication

CIKM 2016

Authors

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