CPMF

A collective pairwise matrix factorization model for upcoming event recommendation

Chun Yi Liu, Chuan Zhou*, Jia Wu, Hongtao Xie, Yue Hu, Li Guo

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationIJCNN 2017
Subtitle of host publicationProceedings of the 2017 International Joint Conference on Neural Networks
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1532-1539
Number of pages8
ISBN (Electronic)9781509061822, 9781509061815
ISBN (Print)9781509061839
DOIs
Publication statusPublished - 30 Jun 2017
Externally publishedYes
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period14/05/1719/05/17

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