Discrete content-aware matrix factorization

Defu Lian, Rui Liu, Yong Ge, Kai Zheng, Xing Xie, Longbing Cao

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

41 Scopus citations

Abstract

Precisely recommending relevant items from massive candidates to a large number of users is an indispensable yet computationally expensive task in many online platforms (e.g., Amazon.com and Netfix.com). A promising way is to project users and items into a Hamming space and then recommend items via Hamming distance. However, previous studies didn't address the cold-start challenges and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Discrete Content-aware Matrix Factorization (DCMF) model, 1) to derive compact yet informative binary codes at the presence of user/item content information; 2) to support the classification task based on a local upper bound of logit loss; 3) to introduce an interaction regularization for dealing with the sparsity issue. We further develop an eficient discrete optimization algorithm for parameter learning. Based on extensive experiments on three real-world datasets, we show that DCFM outperforms the state-of-the-arts on both regression and classification tasks.

Original languageEnglish (US)
Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages325-334
Number of pages10
ISBN (Electronic)9781450348874
DOIs
StatePublished - Aug 13 2017
Externally publishedYes
Event23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada
Duration: Aug 13 2017Aug 17 2017

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F129685

Conference

Conference23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
Country/TerritoryCanada
CityHalifax
Period8/13/178/17/17

Keywords

  • Collaborative filtering
  • Content-based filtering
  • Discrete hashing
  • Recommendation

ASJC Scopus subject areas

  • Software
  • Information Systems

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