Joint Event-Partner Recommendation in Event-Based Social Networks

With the prevalent trend of combining online and offline interactions among users in event-based social networks (EBSNs), event recommendation has become an essential means to help people discover new interesting events to attend. However, existing literatures on event recommendations ignore the social attribute of events: people prefer to attend events with their friends or family rather than alone. Therefore, we propose a new recommendation paradigm: joint event-partner recommendation that focuses on recommending event-partner pairs to users. In this paper, we focus on the new problem of joint event-partner recommendation in EBSNs, which is extremely challenging due to the intrinsic cold-start property of events, the complex decision-making process for choosing event-partner pairs and the huge prediction space of event-partner combinations. We propose a generic graph-based embedding model (GEM) to collectively embed all the observed relations among users, events, locations, time and text content in a shared low-dimension space, which is able to leverage the correlation between events and their associated content and contextual information to address the cold-start issue effectively. To accelerate the convergence of GEM and improve its modeling accuracy, an adaptive noise sampler is developed to generate adversarial negative samples in the model optimization. Besides, to speed up the online recommendation, we propose a novel space transformation method to project each event-partner pair to one point in a new space and then develop effective space pruning and efficient online recommendation techniques. We conduct comprehensive experiments on our created real benchmark datasets, and the experimental results demonstrate the superiority of our proposals in terms of recommendation effectiveness, efficiency and scalability