HDNN-CF: A hybrid deep neural networks collaborative filtering architecture for event recommendation

Along with the rise of Event-Based Social Networks (EBSNs), event recommendation has become an increasing important problem. However, unlike recommending usual items, such as movies or music, event recommendation suffers from severe cold-start problem, because most events in EBSNs are typically short-lived and registered by only a few users. Additionally, the available feedbacks for events are implicit feedbacks. In this work, we propose a Hybrid Deep Neural Networks Collaborative Filtering Architecture (HDNN-CF) that collaboratively makes use of the events' semantic information and users' implicit feedbacks for event recommendation. Specifically, we extend state-of-the-art method AutoRec to model implicit feedbacks by proposing Probabilistic AutoRec (PAutoRec). We collaboratively train a Stacked Denoise AutoEncoder (SDAE) to learn the deep representation of the semantic information and a PAutoRec to collaborative filter based on implicit feedbacks. Extensive experiments on a real large scale dataset Meetup show that HDNN-CF significantly outperforms state-of-the-art methods by 10% on recall of top 30 recommendations.