ABSTRACT
Recommender systems usually need to compare a large number of items before users' most preferred ones can be found This process can be very costly if recommendations are frequently made on large scale datasets. In this paper, a novel hashing algorithm, named Preference Preserving Hashing (PPH), is proposed to speed up recommendation. Hashing has been widely utilized in large scale similarity search (e.g. similar image search), and the search speed with binary hashing code is significantly faster than that with real-valued features. However, one challenge of applying hashing to recommendation is that, recommendation concerns users' preferences over items rather than their similarities. To address this challenge, PPH contains two novel components that work with the popular matrix factorization (MF) algorithm. In MF, users' preferences over items are calculated as the inner product between the learned real-valued user/item features. The first component of PPH constrains the learning process, so that users' preferences can be well approximated by user-item similarities. The second component, which is a novel quantization algorithm,generates the binary hashing code from the learned real-valued user/item features. Finally, recommendation can be achieved efficiently via fast hashing code search. Experiments on three real world datasets show that the recommendation speed of the proposed PPH algorithm can be hundreds of times faster than original MF with real-valued features, and the recommendation accuracy is significantly better than previous work of hashing for recommendation.
- J. Bennett and S. Lanning. The netflix prize. KDD Cup and Workshop, 2007.Google Scholar
- S. Chen, B. Ma, and K. Zhang. On the similarity metric and the distance metric. Theoretical Computer Science, pages 2365--2376, 2009. Google ScholarDigital Library
- W. Chen, W. Hsu, and M. L. Lee. Modeling users' receptiveness over time for recommendation. SIGIR, pages 373--382, 2013. Google ScholarDigital Library
- A. Das, M. Datar, A. Garg, and S. Rajaram. Google news personalization: Scalable online collaborative filtering. WWW, pages 271--280, 2007. Google ScholarDigital Library
- R. Gemulla, P. Haas, E. Nijkamp, and Y. Sismanis. Large-scale matrix factorization with distributed stochastic gradient descent. KDD, pages 69--77, 2011. Google ScholarDigital Library
- A. Gionis, P. Indyk, , and R. Motwani. Similarity search in high dimensions via hashing. VLDB, pages 518--529, 1999. 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, pages 2916--2929, 2012. Google ScholarDigital Library
- K. Grauman and R. Fergus. Learning binary hash codes for large-scale image search. MLCV, pages 49--87, 2013.Google ScholarCross Ref
- K. Järvelin and J. Kekäläinen. Ir evaluation methods for retrieving highly relevant documents. SIGIR, 2000. Google ScholarDigital Library
- A. Karatzoglou, A. Smola, and M. Weimer. Collaborative filtering on a budget. AISTAT, pages 389--396, 2010.Google Scholar
- N. Koenigstein, P. Ram, and Y. Shavitt. Efficient retrieval of recommendations in a matrix factorization framework. CIKM, pages 535--544, 2012. Google ScholarDigital Library
- W. Kong, W. Li, and M. Guo. Manhattan hashing for large-scale image retrieval. SIGIR, pages 45--54, 2012. Google ScholarDigital Library
- Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. KDD, pages 426--434, 2008. Google ScholarDigital Library
- Y. Koren, R. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, pages 30--37, 2009. Google ScholarDigital Library
- B. Kulis and K. Grauman. Kernelized locality-sensitive hashing for scalable image search. ICCV, 2009.Google ScholarCross Ref
- M. Li, X. Chen, X. Li, B. Ma, and P. M. Vit$\acutea$nyi. The similarity metric. IEEE Trans on Information Theory, pages 3250--3264, 2004. Google ScholarDigital Library
- G. Linden, B. Smith, and J. York. Amazon.com recommendations item-to-item collaborative filtering. IEEE Internet Computing, pages 76--80, 2003. Google ScholarDigital Library
- T. Liu, A. W. Moore, A. Gray, and K. Yang. An investigation of practical approximate nearest neighbor algorithms. NIPS, 2005.Google Scholar
- W. Liu, J. Wang, R. Ji, Y. Jiang, and S. Chang. Supervised hashing with kernels. CVPR, pages 2074--2081, 2012. Google ScholarDigital Library
- W. Liu, J. Wang, Y. Mu, S. Kumar, and S. Chang. Compact hyperplane hashing with bilinear functions. ICML, 2012.Google ScholarDigital Library
- M. Norouzi, A. Punjani, and D. J. Fleet. Fast search in hamming space with multi-index hashing. In CVPR, pages 3108--3115, 2012. Google ScholarDigital Library
- E. Ntoutsi, K. Stefanidis, K. Nørvåg, , and H.-P. Kriegel. Fast group recommendations by applying user clustering. Int'l Conf. on Conceptual Modeling, pages 126--140, 2012. Google ScholarDigital Library
- S. Rendle, Z. Gantner, C. Freudenthaler, and L. Schmidt-Thieme. Fast context-aware recommendations with factorization machines. SIGIR, pages 635--644, 2011. Google ScholarDigital Library
- R. Salakhutdinov and G. Hinton. Semantic hashing. SIGIR, pages 969--978, 2007. Google ScholarDigital Library
- K. Sugiyama, K. Hatano, and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. WWW, pages 675--684, 2004. Google ScholarDigital Library
- M. N. Volkovs and R. S. Zemel. Collaborative ranking with 17 parameters. NIPS, pages 2303--2311, 2012.Google Scholar
- J. Wang, S. Kumar, and S. Chang. Semi-supervised hashing for large-scale search. TPAMI, pages 2393--2406, 2012. Google ScholarDigital Library
- Q. Wang, L. Ruan, Z. Zhang, and L. Si. Learning compact hashing codes for efficient tag completion and prediction. CIKM, pages 1789--1794, 2013. Google ScholarDigital Library
- Q. Wang, D. Zhang, and L. Si. Semantic hashing using tags and topic modeling. SIGIR, pages 213--222, 2013. Google ScholarDigital Library
- M. Weimer, A. Karatzoglou, Q. Le, and A. Smola. $\textrmCOFI^RANK$: Maximum margin matrix factorization for collaborative ranking. NIPS, 2007.Google Scholar
- Y. Weiss, A. Torralba, and R. Fergus. Spectral hashing. NIPS, 2008.Google ScholarDigital Library
- X. Yang, H. Steck, Y. Guo, and Y. Liu. On top-k recommendation using social networks. RecSys, pages 67--74, 2012. Google ScholarDigital Library
- H. Yu, C. Hsieh, S. Si, and I. Dhillon. Scalable coordinate descent approaches to parallel matrix factorization for recommender systems. ICDM, pages 765--774, 2012. Google ScholarDigital Library
- D. Zhang, F. Wang, and L. Si. Composite hashing with multiple information sources. SIGIR, pages 225--234, 2011. Google ScholarDigital Library
- D. Zhang, J. Wang, D. Cai, and J. Lu. Self-taught hashing for fast similarity search. SIGIR, pages 18--25, 2010. Google ScholarDigital Library
- G. Zhao, M. L. Lee, W. Hsu, and W. Chen. Increasing temporal diversity with purchase intervals. SIGIR, pages 165--174, 2012. Google ScholarDigital Library
- K. Zhou and H. Zha. Learning binary codes for collaborative filtering. KDD, pages 498--506, 2012. Google ScholarDigital Library
- P. Zigoris and Y. Zhang. Bayesian adaptive user profiling with explicit & implicit feedback. CIKM, pages 397--404, 2006. Google ScholarDigital Library
Index Terms
- Preference preserving hashing for efficient recommendation
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