Improving Smart Conference Participation Through Socially Aware Recommendation

This paper addresses recommending presentation sessions at smart conferences to participants. We propose a venue recommendation algorithm: socially aware recommendation of venues and environments (SARVE). SARVE computes correlation and social characteristic information of conference participants. In order to model a recommendation process using distributed community detection, SARVE further integrates the current context of both the smart conference community and participants. SARVE recommends presentation sessions that may be of high interest to each participant. We evaluate SARVE using a real-world dataset. In our experiments, we compare SARVE with two related state-of-the-art methods, namely context-aware mobile recommendation services and conference navigator (recommender) model. Our experimental results show that in terms of the utilized evaluation metrics, i.e., precision, recall, and f-measure, SARVE achieves more reliable and favorable social (relations and context) recommendation results.