ABSTRACT
We address the efficiency problem of Collaborative Filtering (CF) by hashing users and items as latent vectors in the form of binary codes, so that user-item affinity can be efficiently calculated in a Hamming space. However, existing hashing methods for CF employ binary code learning procedures that most suffer from the challenging discrete constraints. Hence, those methods generally adopt a two-stage learning scheme composed of relaxed optimization via discarding the discrete constraints, followed by binary quantization. We argue that such a scheme will result in a large quantization loss, which especially compromises the performance of large-scale CF that resorts to longer binary codes. In this paper, we propose a principled CF hashing framework called Discrete Collaborative Filtering (DCF), which directly tackles the challenging discrete optimization that should have been treated adequately in hashing. The formulation of DCF has two advantages: 1) the Hamming similarity induced loss that preserves the intrinsic user-item similarity, and 2) the balanced and uncorrelated code constraints that yield compact yet informative binary codes. We devise a computationally efficient algorithm with a rigorous convergence proof of DCF. Through extensive experiments on several real-world benchmarks, we show that DCF consistently outperforms state-of-the-art CF hashing techniques, e.g, though using only 8 bits, DCF is even significantly better than other methods using 128 bits.
- G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. TKDE, 2005. Google ScholarDigital Library
- D. Agarwal, B.-C. Chen, and P. Elango. Fast online learning through offline initialization for time-sensitive recommendation. In KDD, 2010. Google ScholarDigital Library
- S. Balakrishnan and S. Chopra. Collaborative ranking. In WSDM, 2012. Google ScholarDigital Library
- J. Bennett and S. Lanning. The netflix prize. In KDD, 2007.Google Scholar
- J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez. Recommender systems survey. Knowledge-Based Systems, 2013. Google ScholarDigital Library
- A. S. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: scalable online collaborative filtering. In WWW, 2007. Google ScholarDigital Library
- A. Gionis, P. Indyk, R. Motwani, et al. Similarity search in high dimensions via hashing. In VLDB, 1999. Google ScholarDigital Library
- K. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 2001. Google ScholarDigital Library
- Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. TPAMI, 2013. Google ScholarDigital Library
- E. F. Harrington. Online ranking/collaborative filtering using the perceptron algorithm. In ICML, 2003.Google Scholar
- J. Håstad. Some optimal inapproximability results. JACM, 2001. Google ScholarDigital Library
- X. He, T. Chen, M.-Y. Kan, and X. Chen. Trirank: Review-aware explainable recommendation by modeling aspects. In CIKM, 2015. Google ScholarDigital Library
- X. H. He, H. Zhang, M.-Y. Kan, and T.-S. Chua. Fast matrix factorization for online recommendation with implicit feedback. In SIGIR, 2016. Google ScholarDigital Library
- A. Karatzoglou, M. Weimer, and A. J. Smola. Collaborative filtering on a budget. In AISTAT, 2010.Google Scholar
- Y. Koren. Collaborative filtering with temporal dynamics. In KDD, 2009. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Computer, 2009. Google ScholarDigital Library
- W. Liu, C. Mu, S. Kumar, and S.-F. Chang. Discrete graph hashing. In NIPS, 2014. Google ScholarDigital Library
- X. Liu, J. He, C. Deng, and B. Lang. Collaborative hashing. In CVPR, 2014. Google ScholarDigital Library
- H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In WSDM, 2011. Google ScholarDigital Library
- J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online learning for matrix factorization and sparse coding. JMLR, 2010. Google ScholarDigital Library
- J. McAuley and J. Leskovec. Hidden factors and hidden topics: Understanding rating dimensions with review text. In RecSys, 2013. Google ScholarDigital Library
- M. Norouzi, A. Punjani, and D. J. Fleet. Fast search in hamming space with multi-index hashing. In CVPR, 2012. Google ScholarDigital Library
- S. Rendle. Scaling factorization machines to relational data. VLDB, 2013. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In UAI, 2009. Google ScholarDigital Library
- B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, 2001. Google ScholarDigital Library
- F. Shen, C. Shen, W. Liu, and H. T. Shen. Supervised discrete hashing. In CVPR, 2015.Google ScholarCross Ref
- M. Volkovs and G. W. Yu. Effective latent models for binary feedback in recommender systems. In SIGIR, 2015. Google ScholarDigital Library
- J. Wang, S. Kumar, and S.-F. Chang. Semi-supervised hashing for large-scale search. TPAMI, 2012. Google ScholarDigital Library
- J. Wang, W. Liu, S. Kumar, and S.-F. Chang. Learning to hash for indexing big data: A survey. Proceedings of the IEEE, 2016.Google ScholarCross Ref
- M. Weimer, A. Karatzoglou, Q. V. Le, and A. Smola. Maximum margin matrix factorization for collaborative ranking. NIPS, 2007.Google Scholar
- Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. In NIPS, 2009.Google ScholarDigital Library
- Z. Zhang, Q. Wang, L. Ruan, and L. Si. Preference preserving hashing for efficient recommendation. In SIGIR, 2014. Google ScholarDigital Library
- K. Zhou and H. Zha. Learning binary codes for collaborative filtering. In KDD, 2012. Google ScholarDigital Library
- Y. Zhou, D. Wilkinson, R. Schreiber, and R. Pan. Large-scale parallel collaborative filtering for the netflix prize. In AAIM, 2008. Google ScholarDigital Library
Index Terms
- Discrete Collaborative Filtering
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