CPMF: A collective pairwise matrix factorization model for upcoming event recommendation

Publication Type:
Conference Proceeding
Citation:
Proceedings of the International Joint Conference on Neural Networks, 2017, 2017-May pp. 1532 - 1539
Issue Date:
2017-06-30
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© 2017 IEEE. Due to the rapid growth of event-based social networks (EBSNs), event recommendation which helps users find their preferred events has become a popular topic. Different from movies or books in conventional recommendation problem, events usually have recommendation lifetimes and almost all the events to be recommended are upcoming, which brings a severe cold start problem. To achieve better event recommendation performance, we formulates multiple interactions among users, events, groups and locations into an unified framework and propose a collective pairwise matrix factorization (CPMF) model to estimate users' pairwise preferences on events, groups and locations. We further develop an efficient stochastic gradient descent algorithm for the model learning. We conduct experiments on real-world Meetup datasets and the experimental results demonstrate that our CPMF model can outperform the state-of-the-art methods.
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