Preference and Constraint Factor Model for Event Recommendation

Newly emerging Event-based Social Network (EBSN) concentrates on connecting both online social relationships and offline local events. For the growing amount of events published on EBSNs, personalized event recommendation becomes essential to help users choose attractive events. But most of existing event recommendation algorithms fail to distinguish constraint factors of users' event participation behaviors from preference factors, which reflects the cost of event participation that hinders users from attending interested events. To take full advantage of influences from contextual information on users' event participation, we differentiate preference and constraint factors which contribute to users' decision for event participation, and extract the soft spatial and temporal constraints from event venue and start time contexts respectively. Then we propose the Preference and Constraint Factor Model (PCFM) based on factorization machine model, using attentive mechanism to weight feature interactions and incorporate latent factors of users and contextual features for personalized perference modeling and event recommendation. Moreover, learning-to-rank techniques are utilized to train PCFM as a ranking model for the implicit feedback nature of responses from users. Extensive experiments evaluate the performance of our proposed recommendation model on real-world EBSN datasets, and demonstrate the outperformance than state-of-art event recommendation methods on many metrics.