Abstract
One-class collaborative filtering with implicit feedback has attracted much attention, mainly due to the widespread of implicit data in real world. Pairwise methods have been shown to be the state-of-the-art methods for one-class collaborative filtering, but the assumption that users prefer observed items to unobserved items may not always hold. Besides, existing pairwise methods may not perform well in terms of Top-N recommendation. In this paper, we propose a new approach called EBPR, which relaxes the former simple pairwise preference assumption by further exploiting the hidden connection in observed items and unobserved items. EBPR can also be used as a basic method and has the extensive applicability, i.e., when combining our model with former pairwise methods, better performance can also be achieved. Empirical studies show that our algorithm outperforms the state-of-the-art methods on four real-world datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
movielens: http://grouplens.org/datasets/movielens/.
- 2.
netflix: http://www.netflixprize.com/.
References
Bobadilla, J., Ortega, F., Hernando, A., et al.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013)
Rendle, S., et al.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. AUAI Press (2009)
Pan, R., et al.: One-class collaborative filtering. In: Eighth IEEE International Conference on Data Mining, ICDM 2008. IEEE (2008)
Pan, W., Li, C.: GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering. In: IJCAI 2013 (2013)
Cremonesi, P., Yehuda, K., Roberto, T.: Performance of recommender algorithms on Top-N recommendation tasks. In: Proceedings of the Fourth ACM Conference on Recommender Systems. ACM (2010)
Du, L., Li, X., Shen, Y.-D.: User graph regularized pairwise matrix factorization for item recommendation. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011. LNCS (LNAI), vol. 7121, pp. 372–385. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25856-5_28
Pan, W., Chen, L.: CoFiSet: collaborative filtering via learning pairwise preferences over item-sets. In: Proceedings of the 2013 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics (2013)
Pan, W., Chen, L.: Group Bayesian personalized ranking with rich interactions for one-class collaborative filtering. Neurocomputing 207, 501–510 (2016)
Zhang, W., et al.: Optimizing Top-N collaborative filtering via dynamic negative item sampling. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2013)
Rendle, S., Freudenthaler, C.: Improving pairwise learning for item recommendation from implicit feedback. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining. ACM (2014)
Zhong, H., et al.: Adaptive pairwise preference learning for collaborative recommendation with implicit feedbacks. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM (2014)
Guo, W., et al.: Personalized ranking with pairwise Factorization Machines. Neurocomputing 214, 191–200 (2016)
Mao, X., Li, Q., Xie, H., Rao, Y.: Popularity tendency analysis of ranking-oriented collaborative filtering from the perspective of loss function. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8421, pp. 451–465. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-05810-8_30
Song, D., Meyer, D.A., Tao, D.: Efficient latent link recommendation in signed networks. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2015)
Krohn-Grimberghe, A., et al.: Multi-relational matrix factorization using Bayesian personalized ranking for social network data. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. ACM (2012)
Zhao, T., McAuley, J., King, I.: Leveraging social connections to improve personalized ranking for collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM (2014)
Qiu, S., et al.: Item group based pairwise preference learning for personalized ranking. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2014)
Chen, X., et al.: HLBPR: a hybrid local Bayesian personal ranking method. In: Proceedings of the 25th International Conference Companion on World Wide Web. International World Wide Web Conferences Steering Committee (2016)
Guo, Y., Wang, X., Xu, C.: CroRank: cross domain personalized transfer ranking for collaborative filtering. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW). IEEE (2015)
Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining. ACM (2010)
Kanagal, B., et al.: Supercharging recommender systems using taxonomies for learning user purchase behavior. Proc. VLDB Endow. 5(10), 956–967 (2012)
Riedel, S., et al.: Relation extraction with matrix factorization and universal schemas. In: HLT-NAACL (2013)
Shi, Y., et al.: TFMAP: optimizing MAP for Top-N context-aware recommendation. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2012)
Shi, Y., et al.: CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: Proceedings of the Sixth ACM Conference on Recommender Systems. ACM (2012)
Sindhwani, V., et al.: One-class matrix completion with low-density factorizations. In: 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE (2010)
Geng, X., et al.: Learning image and user features for recommendation in social networks. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
He, X., et al.: TriRank: review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM (2015)
Acknowledgments
This work was supported by the Ministry of Science and Technology of China (Grant No. 2017YFC0804003), the National Natural Science Foundation of China (Grant No. 61503357), and Science and Technology Innovation Committee Foundation of Shenzhen (Grant Nos. ZDSYS201703031748284, and JCYJ20170307105521943).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, Y., Yuan, B., Tang, K. (2017). Enhanced Pairwise Learning for Personalized Ranking from Implicit Feedback. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2017. Communications in Computer and Information Science, vol 791. Springer, Singapore. https://doi.org/10.1007/978-981-10-7179-9_46
Download citation
DOI: https://doi.org/10.1007/978-981-10-7179-9_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7178-2
Online ISBN: 978-981-10-7179-9
eBook Packages: Computer ScienceComputer Science (R0)