RecEvent: Multiple Features Hybrid Event Recommendation in Social Networks.

The large volume of event information makes it difficult for users to find interesting events in social networks. Therefore, we would like to develop an intelligent event recommendation to reduce information overload. Specifically, by exploring the behavior of users during the selection process, we are able to find particular rules associated with various event attributes which reflect the willingness of users. However, traditional event recommendations in social networks mainly concern the basic items like time and location. It is noted that few studies have yielded specific aspects such as the influence and spread capability of events and hosts. In this paper, we propose an event recommender approach fusing multiple features that can provide users with customized contents. To be specific, we consider hybridizing features including event influence, host impact, fee, social relationship and spatiotemporal characteristics. In order to achieve better performance, we concern the match degree between user and event properties especially in terms of their content and impact. Based on the improved idea of RankNet with neural networks, we build a Learning to Rank algorithm to reveal the importance of each feature. We rectify the problem of data sparse and cold start to grasp the balance of accuracy and novelty. Extensive experiments on datasets demonstrate that our method achieves promising results in comparison with other schemes.