Session-based recommendations with recurrent neural networks

4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings

Volumn , Issue , 2016, Pages

  Hidasi, Balázs ^a   Karatzoglou, Alexandros ^b   Baltrunas, Linas ^c   Tikk, Domonkos ^a  

^a Gravity Research and Development Inc   (Hungary)

^b TELEFONICA RESEARCH   (Spain)

^c NETFLIX INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FACTORIZATION; RECOMMENDER SYSTEMS;

LOSS FUNCTIONS; MATRIX FACTORIZATIONS; NETFLIX; RECURRENT NEURAL NETWORK (RNN); SPECIFIC PROBLEMS; USER HISTORY;

RECURRENT NEURAL NETWORKS;

EID: 85083953993     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Cho, Kyunghyun, van Merriënboer, Bart, Bahdanau, Dzmitry, and Bengio, Yoshua. On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259, 2014.
2. Dauphin, Yann N, de Vries, Harm, Chung, Junyoung, and Bengio, Yoshua. Rmsprop and equi-librated adaptive learning rates for non-convex optimization. arXiv preprint arXiv:1502.04390, 2015.
3. Davidson, James, Liebald, Benjamin, Liu, Junning, et al. The YouTube video recommendation system. In Recsys’10: ACM Conf. on Recommender Systems, pp. 293–296, 2010. ISBN 978-1-60558-906-0.
4. Duchi, John, Hazan, Elad, and Singer, Yoram. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121–2159, 2011.
5. Hidasi, B. and Tikk, D. Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. In ECML-PKDD’12, Part II, number 7524 in LNCS, pp. 67–82. Springer, 2012.
6. Hidasi, Balázs and Tikk, Domonkos. General factorization framework for context-aware recommendations. Data Mining and Knowledge Discovery, pp. 1–30, 2015. ISSN 1384-5810. doi: 10.1007/s10618-015-0417-y. URL http://dx.doi.org/10.1007/s10618-015-0417-y.
7. Hinton, Geoffrey, Deng, Li, Yu, Dong, Dahl, George E, Mohamed, Abdel-rahman, Jaitly, Navdeep, Senior, Andrew, Vanhoucke, Vincent, Nguyen, Patrick, Sainath, Tara N, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29(6):82–97, 2012.
8. Koren, Y. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In SIGKDD’08: ACM Int. Conf. on Knowledge Discovery and Data Mining, pp. 426–434, 2008.
9. Koren, Yehuda, Bell, Robert, and Volinsky, Chris. Matrix factorization techniques for recommender systems. Computer, 42(8):30–37, 2009.
10. Linden, G., Smith, B., and York, J. Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1):76–80, 2003.
11. Liu, Qiwen, Chen, Tianjian, Cai, Jing, and Yu, Dianhai. Enlister: Baidu’s recommender system for the biggest Chinese Q&A website. In RecSys-12: Proc. of the 6th ACM Conf. on Recommender Systems, pp. 285–288, 2012.
12. th Conf. on Uncertainty in Artificial Intelligence, pp. 452–461, 2009. ISBN 978-0-9749039-5-8.
13. Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael S., Berg, Alexander C., and Li, Fei-Fei. Imagenet large scale visual recognition challenge. CoRR, abs/1409.0575, 2014. URL http://arxiv.org/abs/1409.0575.
14. Salakhutdinov, Ruslan, Mnih, Andriy, and Hinton, Geoffrey. Restricted boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning, pp. 791–798. ACM, 2007.
15. Sarwar, Badrul, Karypis, George, Konstan, Joseph, and Riedl, John. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pp. 285–295. ACM, 2001.
16. Shani, Guy, Brafman, Ronen I, and Heckerman, David. An mdp-based recommender system. In Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence, pp. 453–460. Morgan Kaufmann Publishers Inc., 2002.
17. Shi, Yue, Karatzoglou, Alexandros, Baltrunas, Linas, Larson, Martha, Oliver, Nuria, and Hanjalic, Alan. Climf: Learning to maximize reciprocal rank with collaborative less-is-more filtering. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys’12, pp. 139–146, New York, NY, USA, 2012. ACM. ISBN 978-1-4503-1270-7. doi: 10.1145/2365952.2365981. URL http://doi.acm.org/10.1145/2365952.2365981.
18. Steck, Harald. Gaussian ranking by matrix factorization. In Proceedings of the 9th ACM Conference on Recommender Systems, RecSys’15, pp. 115–122, New York, NY, USA, 2015. ACM. ISBN 978-1-4503-3692-5. doi: 10.1145/2792838.2800185. URL http://doi.acm.org/10.1145/2792838.2800185.
19. Van den Oord, Aaron, Dieleman, Sander, and Schrauwen, Benjamin. Deep content-based music recommendation. In Advances in Neural Information Processing Systems, pp. 2643–2651, 2013.
20. Wang, Hao, Wang, Naiyan, and Yeung, Dit-Yan. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’15, pp. 1235–1244, New York, NY, USA, 2015. ACM.
21. Weimer, Markus, Karatzoglou, Alexandros, Le, Quoc Viet, and Smola, Alex. Maximum margin matrix factorization for collaborative ranking. Advances in neural information processing systems, 2007.