Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 12534))
Included in the following conference series:
2203Accesses
Abstract
Heterogeneous one-class collaborative filtering (HOCCF) is a recent and important recommendation problem which involves two different types of one-class feedback such as purchases and examinations. In this paper, we propose a generic asymmetric pairwise preference assumption and a novel like-minded user-group construction strategy for the HOCCF problem. Specifically, our generic assumption contains six different pairwise preference relations derived from the heterogeneous feedback, where we introduce a series of weighting strategies to make our assumption more reasonable. Our group construction strategy introduces richer interactions within user-groups, which is expected to learn the users’ preference more accurately. We then design a novel recommendation model calledasymmetricpairwisepreferencelearning (APPLE). Extensive empirical studies show that our APPLE can recommend items significantly more accurately than the closely related state-of-the-art methods on three real-world datasets.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 11439
- Price includes VAT (Japan)
- Softcover Book
- JPY 14299
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, C., Zhang, M., Zhang, Y., Ma, W., Liu, Y., Ma, S.: Efficient heterogeneous collaborative filtering without negative sampling for recommendation. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 19–26 (2020)
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Trans. Inf. Syst.22(1), 143–177 (2004)
Ding, J., et al.: Improving implicit recommender systems with view data. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. IJCAI 2018, pp. 3343–3349 (2018)
He, X., He, Z., Song, J., Liu, Z., Jiang, Y.G., Chua, T.S.: NAIS: neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng.30(12), 2354–2366 (2018)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. WWW 2017, pp. 173–182 (2017)
Hu, Y., Yehuda, K., Chris, V.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining. ICDM 2008, pp. 263–272 (2008)
Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-N recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2013, pp. 659–667 (2013)
Ouyang, S., Li, L., Pan, W., Ming, Z.: Asymmetric Bayesian personalized ranking for one-class collaborative filtering. In: Proceedings of the 13th ACM Conference on Recommender Systems. RecSys 2019, pp. 373–377 (2019)
Pan, R., et al.: One-class collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining. ICDM 2008, pp. 502–511 (2008)
Pan, W., Chen, L., Ming, Z.: Personalized recommendation with implicit feedback via learning pairwise preferences over item-sets. Knowl. Inf. Syst.58(2), 295–318 (2019).https://doi.org/10.1007/s10115-018-1154-5
Pan, W., Liu, M., Ming, Z.: Transfer learning for heterogeneous one-class collaborative filtering. IEEE Intell. Syst.31(4), 43–49 (2016)
Qiu, H., Liu, Y., Guo, G., Sun, Z., Zhang, J., Nguyen, H.T.: BPRH: Bayesian personalized ranking for heterogeneous implicit feedback. Inf. Sci.453, 80–98 (2018)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. UAI 2009, pp. 452–461 (2009)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Annual Conference on Neural Information Processing Systems. NeurIPS 2007, pp. 1257–1264 (2007)
Acknowledgement
We thank the support of National Natural Science Foundation of China Nos. 61872249, 61836005 and 61672358. Weike Pan and Zhong Ming are the corresponding authors for this work.
Author information
Authors and Affiliations
National Engineering Laboratory for Big Data System Computing Technology, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Yongxin Ni, Zhuoxin Zhan, Weike Pan & Zhong Ming
- Yongxin Ni
You can also search for this author inPubMed Google Scholar
- Zhuoxin Zhan
You can also search for this author inPubMed Google Scholar
- Weike Pan
You can also search for this author inPubMed Google Scholar
- Zhong Ming
You can also search for this author inPubMed Google Scholar
Corresponding authors
Correspondence toWeike Pan orZhong Ming.
Editor information
Editors and Affiliations
Department of AI, Ping An Life, Shenzhen, China
Haiqin Yang
Faculty of Information Technology, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand
Kitsuchart Pasupa
City University of Hong Kong, Kowloon, Hong Kong
Andrew Chi-Sing Leung
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
James T. Kwok
School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand
Jonathan H. Chan
The Chinese University of Hong Kong, New Territories, Hong Kong
Irwin King
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ni, Y., Zhan, Z., Pan, W., Ming, Z. (2020). Asymmetric Pairwise Preference Learning for Heterogeneous One-Class Collaborative Filtering. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_34
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-030-63835-1
Online ISBN:978-3-030-63836-8
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative