This paper investigates various recommendation algorithms to recommend relevant talks to attendees of research conferences. We explored three sources of information to generate recommendations: users' preference about items (i.e. talks), users' social network and content of items. In order to find out what is the best recommendation approach, we explored a diverse set of algorithms from non-personalized community vote-based recommendations and collaborative filtering recommend-ations to hybrid recommendations such as social network-based recommendation boosted by content information of items. We found that social network-based recommendations fused with content information and non-personalized community vote-based recommendations performed the best. Moreover, for cold-start users who have insufficient number of items to express their preferences, the recommendations based on their social connections generated significantly better predictions than other approaches.
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