Yan Zhang
Hongzhi Yin
ABSTRACT
Cold-start problem and recommendation efficiency have been regarded as two crucial challenges in the recommender system. In this paper, we propose a hashing based deep learning framework called Discrete Deep Learning (DDL), to map users and items to Hamming space, where a user»s preference for an item can be efficiently calculated by Hamming distance, and this computation scheme significantly improves the efficiency of online recommendation. Besides, DDL unifies the user-item interaction information and the item content information to overcome the issues of data sparsity and cold-start. To be more specific, to integrate content information into our DDL framework, a deep learning model, Deep Belief Network (DBN), is applied to extract effective item representation from the item content information. Besides, the framework imposes balance and irrelevant constraints on binary codes to derive compact but informative binary codes. Due to the discrete constraints in DDL, we propose an efficient alternating optimization method consisting of iteratively solving a series of mixed-integer programming subproblems. Extensive experiments have been conducted to evaluate the performance of our DDL framework on two different Amazon datasets, and the experimental results demonstrate the superiority of DDL over the state-of-the-art methods regarding online recommendation efficiency and cold-start recommendation accuracy.
- Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, and Yixin Chen. 2015. Compressing neural networks with the hashing trick. In International Conference on Machine Learning. 2285--2294. Google Scholar
Digital Library
- Wen-Yen Chen, Jon-Chyuan Chu, Junyi Luan, Hongjie Bai, Yi Wang, and Edward Y Chang. 2009. Collaborative filtering for orkut communities: discovery of user latent behavior. In Proceedings of the 18th international conference on World wide web. ACM, 681--690. Google ScholarDigital Library
- Abhinandan S Das, Mayur Datar, Ashutosh Garg, and Shyam Rajaram. 2007. Google news personalization: scalable online collaborative filtering. In Proc. of WWW. ACM, 271--280. Google ScholarDigital Library
- Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the twentieth annual symposium on Computational geometry. ACM, 253--262. Google ScholarDigital Library
- Yunchao Gong, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin. 2013. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 12 (2013), 2916--2929. Google ScholarDigital Library
- Johan H?aåstad. 2001. Some optimal inapproximability results. J. ACM 48, 4 (2001), 798--859. Google ScholarDigital Library
- Ruining He and Julian McAuley. 2016. VBPR: visual bayesian personalized ranking from implicit feedback. In Thirtieth AAAI Conference on Artificial Intelligence. Google ScholarDigital Library
- Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. 2006. A fast learning algorithm for deep belief nets. Neural computation 18, 7 (2006), 1527--1554. Google ScholarDigital Library
- Kurt Hornik, Maxwell Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural networks 2, 5 (1989), 359--366. Google ScholarDigital Library
- Alexandros Karatzoglou, Markus Weimer, and Alex J Smola. 2010. Collaborative filtering on a budget. In International Conference on Artificial Intelligence and Statistics. 389--396.Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 426--434. Google ScholarDigital Library
- Yehuda Koren and Robert Bell. 2011. Advances in collaborative filtering. In Recommender systems handbook. Springer, 145--186.Google ScholarDigital Library
- Hao Ma, Haixuan Yang, Michael R Lyu, and Irwin King. 2008. Sorec: social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM conference on Information and knowledge management. ACM, 931--940. Google ScholarDigital Library
- Manos Papagelis, Dimitris Plexousakis, and Themistoklis Kutsuras. 2005. Alleviating the sparsity problem of collaborative filtering using trust inferences. In International Conference on Trust Management. Springer, 224--239. Google ScholarDigital Library
- Martin F Porter. 1980. An algorithm for suffix stripping. Program 14, 3 (1980), 130--137.Google ScholarCross Ref
- Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, 452--461. Google ScholarDigital Library
- Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization.. In Nips, Vol. 1. 2--1. Google ScholarDigital Library
- Andrew I Schein, Alexandrin Popescul, Lyle H Ungar, and David M Pennock. 2002. Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 253--260. Google ScholarDigital Library
- Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. 2015. Supervised Discrete Hashing. In CVPR. 37--45.Google Scholar
- Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Nuria Oliver, and Alan Hanjalic. 2012. CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the sixth ACM conference on Recommender systems. ACM, 139--146. Google ScholarDigital Library
- Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research 11, Dec (2010), 3371--3408. Google ScholarDigital Library
- Ellen M Voorhees et al. 1999. The TREC-8 Question Answering Track Report. In Trec, Vol. 99. 77--82.Google Scholar
- Chong Wang and David M Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 448--456. Google ScholarDigital Library
- Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1235--1244. Google ScholarDigital Library
- Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. 2012. Semisupervised hashing for large-scale search. IEEE TPAMI 34, 12 (2012), 2393--2406. Google ScholarDigital Library
- Jun Wang, Wei Liu, Sanjiv Kumar, and Shih-Fu Chang. 2016. Learning to hash for indexing big dataA survey. Proc. of the IEEE 104, 1 (2016), 34--57.Google ScholarCross Ref
- Weiqing Wang, Hongzhi Yin, Shazia Sadiq, Ling Chen, Min Xie, and Xiaofang Zhou. 2016. Spore: A sequential personalized spatial item recommender system. In Data Engineering (ICDE), 2016 IEEE 32nd International Conference on. IEEE, 954--965.Google ScholarCross Ref
- Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, and Xiaofang Zhou. 2015. Dynamic user modeling in social media systems. ACM Transactions on Information Systems (TOIS) 33, 3 (2015), 10. Google ScholarDigital Library
- Hongzhi Yin, Bin Cui, Yizhou Sun, Zhiting Hu, and Ling Chen. 2014. Lcars: A spatial item recommender system. ACM Transactions on Information Systems (TOIS) 32, 3 (2014), 11. Google ScholarDigital Library
- Hongzhi Yin, Bin Cui, Xiaofang Zhou, Weiqing Wang, Zi Huang, and Shazia Sadiq. 2016. Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. ACM Transactions on Information Systems (TOIS) 35, 2 (2016), 11. Google ScholarDigital Library
- Hongzhi Yin, Yizhou Sun, Bin Cui, Zhiting Hu, and Ling Chen. 2013. Lcars: a location-content-aware recommender system. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 221--229. Google ScholarDigital Library
- Hongzhi Yin, Weiqing Wang, Hao Wang, Ling Chen, and Xiaofang Zhou. 2017. Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation. IEEE Transactions on Knowledge and Data Engineering 29, 11 (2017), 2537--2551.Google ScholarDigital Library
- Hongzhi Yin, Xiaofang Zhou, Bin Cui, Hao Wang, Kai Zheng, and Quoc Viet Hung Nguyen. 2016. Adapting to user interest drift for poi recommendation. IEEE Transactions on Knowledge and Data Engineering 28, 10 (2016), 2566--2581. Google ScholarDigital Library
- Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 353--362. Google ScholarDigital Library
- Hanwang Zhang, Fumin Shen, Wei Liu, Xiangnan He, Huanbo Luan, and Tat-Seng Chua. 2016. Discrete collaborative filtering. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 325--334. Google ScholarDigital Library
- Yan Zhang, Defu Lian, and Guowu Yang. 2017. Discrete Personalized Ranking for Fast Collaborative Filtering from Implicit Feedback. (2017).Google Scholar
- Yan Zhang, Guowu Yang, Lin Hu, Hong Wen, and Jinsong Wu. 2017. Dot-product based preference preserved hashing for fast collaborative filtering. In Communications (ICC), 2017 IEEE International Conference on. IEEE, 1--6.Google ScholarCross Ref
- Zhiwei Zhang, Qifan Wang, Lingyun Ruan, and Luo Si. 2014. Preference preserving hashing for efficient recommendation. In Proc. of SIGIR. ACM, 183--192. Google ScholarDigital Library
- Ke Zhou and Hongyuan Zha. 2012. Learning binary codes for collaborative filtering. In Proc. of ACM SIGKDD. ACM, 498-- 506. Google ScholarDigital Library
Index Terms
- Discrete Deep Learning for Fast Content-Aware Recommendation
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