Dual-regularized one-class collaborative filtering

Yuan Yao, Hanghang Tong, Guo Yan, Feng Xu, Xiang Zhang, Boleslaw K. Szymanski, Jian Lu

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

31 Citations (Scopus)

Abstract

Collaborative filtering is a fundamental building block in many rec-ommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples. In this paper, we propose a dual-regularized model for one-class collaborative filtering. In particular, we address the ambiguity challenge by integrating two state-of-the-art one-class collaborative filtering methods to enjoy the best of both worlds. We tackle the sparseness challenge by exploiting the side information from both users and items. Moreover, we propose efficient algorithms to solve the proposed model. Extensive experimental evaluations on two real data sets demonstrate that our method achieves significant improvement over the state-of-the-art methods. Overall, the proposed method leads to 7.9% - 21.1% improvement over its best known competitors in terms of prediction accuracy, while enjoying the linear scalability.

Original languageEnglish (US)
Title of host publicationCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery, Inc
Pages759-768
Number of pages10
ISBN (Electronic)9781450325981
DOIs
StatePublished - Nov 3 2014
Event23rd ACM International Conference on Information and Knowledge Management, CIKM 2014 - Shanghai, China
Duration: Nov 3 2014Nov 7 2014

Publication series

NameCIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management

Other

Other23rd ACM International Conference on Information and Knowledge Management, CIKM 2014
CountryChina
CityShanghai
Period11/3/1411/7/14

Fingerprint

Collaborative filtering
Stars
Scalability
Feedback

All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Computer Science Applications
  • Information Systems

Cite this

Yao, Y., Tong, H., Yan, G., Xu, F., Zhang, X., Szymanski, B. K., & Lu, J. (2014). Dual-regularized one-class collaborative filtering. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management (pp. 759-768). (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management). Association for Computing Machinery, Inc. https://doi.org/10.1145/2661829.2662042
Yao, Yuan ; Tong, Hanghang ; Yan, Guo ; Xu, Feng ; Zhang, Xiang ; Szymanski, Boleslaw K. ; Lu, Jian. / Dual-regularized one-class collaborative filtering. CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2014. pp. 759-768 (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management).
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title = "Dual-regularized one-class collaborative filtering",
abstract = "Collaborative filtering is a fundamental building block in many rec-ommender systems. While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class setting where user feedback is implicitly expressed (e.g., views in news recommendation and video recommendation). The main challenges in such one-class setting include the ambiguity of the unobserved examples and the sparseness of existing positive examples. In this paper, we propose a dual-regularized model for one-class collaborative filtering. In particular, we address the ambiguity challenge by integrating two state-of-the-art one-class collaborative filtering methods to enjoy the best of both worlds. We tackle the sparseness challenge by exploiting the side information from both users and items. Moreover, we propose efficient algorithms to solve the proposed model. Extensive experimental evaluations on two real data sets demonstrate that our method achieves significant improvement over the state-of-the-art methods. Overall, the proposed method leads to 7.9{\%} - 21.1{\%} improvement over its best known competitors in terms of prediction accuracy, while enjoying the linear scalability.",
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Yao, Y, Tong, H, Yan, G, Xu, F, Zhang, X, Szymanski, BK & Lu, J 2014, Dual-regularized one-class collaborative filtering. in CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management, Association for Computing Machinery, Inc, pp. 759-768, 23rd ACM International Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, 11/3/14. https://doi.org/10.1145/2661829.2662042

Dual-regularized one-class collaborative filtering. / Yao, Yuan; Tong, Hanghang; Yan, Guo; Xu, Feng; Zhang, Xiang; Szymanski, Boleslaw K.; Lu, Jian.

CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc, 2014. p. 759-768 (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management).

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

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Yao Y, Tong H, Yan G, Xu F, Zhang X, Szymanski BK et al. Dual-regularized one-class collaborative filtering. In CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management. Association for Computing Machinery, Inc. 2014. p. 759-768. (CIKM 2014 - Proceedings of the 2014 ACM International Conference on Information and Knowledge Management). https://doi.org/10.1145/2661829.2662042