How to Impute Missing Ratings?: Claims, Solution, and Its Application to Collaborative Filtering

ABSTRACT

Data sparsity is one of the biggest problems faced by collaborative filtering used in recommender systems. Data imputation alleviates the data sparsity problem by inferring missing ratings and imputing them to the original rating matrix. In this paper, we identify the limitations of existing data imputation approaches and suggest three new claims that all data imputation approaches should follow to achieve high recommendation accuracy. Furthermore, we propose a deep-learning based approach to compute imputed values that satisfies all three claims. Based on our hypothesis that most pre-use preferences (e.g., impressions) on items lead to their post-use preferences (e.g., ratings), our approach tries to understand via deep learning how pre-use preferences lead to post-use preferences differently depending on the characteristics of users and items. Through extensive experiments on real-world datasets, we verify our three claims and hypothesis, and also demonstrate that our approach significantly outperforms existing state-of-the-art approaches.

References

1. G. Adomavicius and A. Tuzhilin. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on Knowledge and Data Engineering Vol. 17 (2005), 734--749.
2. S. Chang, F. M. Harper, and L. G. Terveen. 2016. Crowd-Based Personalized Natural Language Explanations for Recommendations Proc. ACM Int'l Conf. on Recommender Systems. 175--182.
3. P. Cremonesi, Y. Koren, and R. Turrin. 2010. Performance of Recommender Algorithms on Top-N Recommendation Tasks Proc. ACM Int'l Conf. on Recommender Systems. 39--46.
4. C. Doersch. 2016. Tutorial on Variational Autoencoders. arXiv preprint arXiv:1606.05908 (2016).
5. B. Sarwar et al.. 2001. Item-Based Collaborative Filtering Recommendation Algorithms Proc. Int'l Conf. on World Wide Web. 285--295.
6. D. Kingma et al.. 2014 a. Semi-supervised Learning with Deep Generative Models Advances in Neural Information Processing Systems. 3581--3589.
7. D. K. Chae et al.. 2018 a. On Identifying k-Nearest Neighbors in Neighborhood Models for Efficient and Effective Collaborative Filtering. Neurocomputing Vol. 278 (2018), 124--143.
8. J. Lee et al.. 2013 a. Alleviating the Sparsity in Collaborative Filtering using Crowdsourcing Proc. of Workshop on Crowdsourcing and Human Computation for Recommender Systems.
9. J. Lee et al.. 2016 a. Improving the Accuracy of Top-N Recommendation using a Preference Model. Information Sciences Vol. 348 (2016), 290--304.
10. J. W. Ha et al.. 2012 a. Top-N Recommendation through Belief Propagation. In Proc. ACM Int'l Conf. on Information and Knowledge Management. 2343--2346.
11. M. H. Jang et al.. 2016 b. PIN-TRUST: Fast Trust Propagation Exploiting Positive, Implicit, and Negative Information. In Proc. ACM Int'l Conf. on Information and Knowledge Management. 629--638.
12. R. Pan et al.. 2008. One-Class Collaborative Filtering. In Proc. IEEE Int'l Conf. on Data Mining. 502--511.
13. S. C. Lee et al.. 2015 a. A Graph-Based Recommendation Framework for Price-Comparison Services Proc. Int'l Conf. on World Wide Web. 59--60.
14. S. C. Lee et al.. 2017. A Single-Step Approach to Recommendation Diversification Proc. Int'l Conf. on World Wide Web. 809--810.
15. S. Rendle et al.. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback Proc. Int'l Conf. on Uncertainty in Artificial Intelligence. 452--461.
16. V. Sindhwani et al.. 2010 a. One-Class Matrix Completion with Low-Density Factorizations Proc. IEEE Int'l Conf. on Data Mining. 1055--1060.
17. W. S. Hwang et al.. 2013 b. Exploiting Trustors as well as Trustees in Trust-Based Recommendation Proc. ACM Int'l Conf. on Information and Knowledge Management. 1893--1896.
18. W. S. Hwang et al.. 2014 b. Data Imputation using a Trust Network for Recommendation Proc. Int'l Conf. on World Wide Web. 299--300.
19. W. S. Hwang et al.. 2015 b. On Exploiting Trustors in Trust-Based Recommendation. Journal of Internet Technology Vol. 16, 4 (2015), 755--765.
20. W. S. Hwang et al.. 2016 c. Efficient Recommendation Methods using Category Experts for a Large Dataset. Information Fusion Vol. 28 (2016), 75--82.
21. W. S. Hwang et al.. 2016 d. “Told You I Didn't Like It”: Exploiting Uninteresting Items for Effective Collaborative Filtering. In Proc. IEEE Int'l Conf. on Data Engineering. 349--360.
22. X. He et al.. 2016 e. Fast Matrix Factorization for Online Recommendation with Implicit Feedback Proc. ACM Int'l Conf. on Research and Development in Information Retrieval. 549--558.
23. Y. C. Lee et al.. 2018 b. gOCCF: Graph-Theoretic One-Class Collaborative Filtering Based on Uninteresting Items Proc. AAAI Conf. on Artificial Intelligence.
24. Y. Li et al.. 2010 b. Improving One-Class Collaborative Filtering by Incorporating rich User Information Proc. ACM Int'l Conf. on Information and Knowledge Management. 959--968.
25. Y. Pu et al.. 2016 f. Variational Autoencoder for Deep Learning of Images, Labels and Captions Advances in Neural Information Processing Systems. 2352--2360.
26. Y. Ren et al.. 2012 b. The Efficient Imputation Method for Neighborhood-Based Collaborative Filtering. In Proc. ACM Int'l Conf. on Information and Knowledge Management. 684--693.
27. Y. Ren et al.. 2013 c. AdaM: Adaptive-Maximum Imputation for Neighborhood-Based Collaborative Filtering. In Proc. IEEE Int'l Conf. on Advances in Social Networks Analysis and Mining. 628--635.
28. Y. Yao et al.. 2014 c. Dual-Regularized One-Class Collaborative Filtering Proc. ACM Int'l Conf. on Information and Knowledge Management. 759--768.
29. Z. Gantner et al.. 2011. MyMediaLite: A Free Recommender System Library. In Proc. ACM Int'l Conf. on Recommender Systems. 305--308.
30. R. He and J. McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In Proc. Int'l Conf. on World Wide Web. 507--517.
31. Y. Hu, Y. Koren, and C. Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets Proc. IEEE Int'l Conf. on Data Mining. 263--272.
32. D. Kingma and M. Welling. 2013. Auto-Encoding Variational Bayes. In arXiv preprint arXiv:1312.6114.
33. Y. Koren. 2008. Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model. In Proc. ACM Int'l Conf. on Knowledge Discovery and Data Mining. 426--434.
34. Y. Koren, R. Bell, and C. Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer Vol. 42 (2009), 30--37.
35. X. Li and J. She. 2017. Collaborative Variational Autoencoder for Recommender Systems Proc. of ACM Int'l Conf. on Knowledge Discovery and Data Mining. 305--314.
36. H. Ma, I. King, and M. R. Lyu. 2007. Effective Missing Data Prediction for Collaborative Filtering Proc. ACM Int'l Conf. on Research and Development in Information Retrieval. 39--46.
37. S. Nathan and J. Tommi. 2003. Weighted Low-Rank Approximations. In Proc. Int'l Conf. on Machine Learning. 720--727.
38. R. Pan and M. Scholz. 2009. Mind the Gaps: Weighting the Unknown in Large-Scale One-Class Collaborative Filtering Proc. ACM Int'l Conf. on Knowledge Discovery and Data Mining. 667--676.
39. W. Pan and L. Chen. 2013. GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering. In Proc. Int'l Joint Conf. on Artificial Intelligence. 2691--2697.
40. S. Rendle and C. Freudenthaler. 2014. Improving Pairwise Learning for Item Recommendation from Implicit Feedback Proc. Int'l Conf. on Web Search and Data Mining. 273--282.
41. R. Salakhutdinov and A. Mnih. 2008. Probabilistic Matrix Factorization. In Advances in Neural Information Processing Systems. 1257--1264.
42. B. Smyth, R. Rafter, and S. Banks. 2016. Harnessing Crowdsourced Recommendation Preference Data from Casual Gameplay Proc. ACM Int'l Conf. on User Modeling Adaptation and Personalization. 95--104.
43. H. Steck. 2010. Training and Testing of Recommender Systems on Data Missing Not at Random Proc. ACM Int'l Conf. on Knowledge Discovery and Data Mining. 713--722.
44. M. Volkovs and G. W. Yu. 2015. Effective Latent Models for Binary Feedback in Recommender Systems Proc. ACM Int'l Conf. on Research and Development in Information Retrieval. 313--322.

Index Terms

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Recommender systems