ABSTRACT
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The improved performance of these losses over alternatives, along with further tricks and refinements described in this work, allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly. We further demonstrate the performance gain of the RNN over baselines in an online A/B test.
- Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, et al. 2016. Theano: A Python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688 (2016).Google Scholar
- Alejandro Bellogin, Pablo Castells, and Ivan Cantador. 2011. Precision-oriented Evaluation of Recommender Systems: An Algorithmic Comparison. In RecSys'11: 5th ACM Conf. on Recommender Systems. 333--336. Google ScholarDigital Library
- Chris J.C. Burges. 2010. From RankNet to LambdaRank to LambdaMART: An Overview. Technical Report. https://www.microsoft.com/en-us/research/ publication/from-ranknet-to-lambdarank-to-lambdamart-an-overview/Google Scholar
- Sotirios Chatzis, Panayiotis Christodoulou, and Andreas S Andreou. 2017. Recurrent Latent Variable Networks for Session-Based Recommendation. arXiv preprint arXiv:1706.04026 (2017).Google Scholar
- Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder--Decoder Approaches. In SSST-8: 8thWorkshop on Syntax, Semantics and Structure in Statistical Translation. 103--111.Google Scholar
- Robin Devooght and Hugues Bersini. 2016. Collaborative filtering with recurrent neural networks. arXiv preprint arXiv:1608.07400 (2016).Google Scholar
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2016. Session-based Recommendations with Recurrent Neural Networks. International Conference on Learning Representations (2016). http://arxiv.org/abs/1511. 06939Google Scholar
- Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. 2016. Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16). ACM, New York, NY, USA, 241--248. Google ScholarDigital Library
- B. Hidasi and D. Tikk. 2012. Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. In ECML-PKDD'12, Part II. Number 7524 in LNCS. Springer, 67--82.Google Scholar
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Shihao Ji, SVN Vishwanathan, Nadathur Satish, Michael J Anderson, and Pradeep Dubey. 2016. Blackout: Speeding up recurrent neural network language models with very large vocabularies. ICLR (2016).Google Scholar
- Yehuda Koren and Joe Sill. 2011. OrdRec: An Ordinal Model for Predicting Personalized Item Rating Distributions. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys '11). ACM, New York, NY, USA, 117--124. Google ScholarDigital Library
- G. Linden, B. Smith, and J. York. 2003. Amazon.com recommendations: Item-toitem collaborative filtering. Internet Computing, IEEE 7, 1 (2003), 76--80. Google ScholarDigital Library
- Qiwen Liu, Tianjian Chen, Jing Cai, and Dianhai Yu. 2012. Enlister: Baidu's Recommender System for the Biggest Chinese Q&A Website. In RecSys-12: Proc. of the 6th ACM Conf. on Recommender Systems. 285--288. Google ScholarDigital Library
- 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 (UAI '09). 452--461. Google ScholarDigital Library
- S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI'09: 25th Conf. on Uncertainty in Artificial Intelligence. 452--461. Google ScholarDigital Library
- Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In WWW:01: 10th Int. Conf. on World Wide Web. 285--295. Google ScholarDigital Library
- 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 (RecSys '12). 139--146. Google ScholarDigital Library
- Yong Kiam Tan, Xinxing Xu, and Yong Liu. 2016. Improved Recurrent Neural Networks for Session-based Recommendations. In Proceedings of the 1stWorkshop on Deep Learning for Recommender Systems (DLRS 2016). ACM, New York, NY, USA, 17--22. Google ScholarDigital Library
- Markus Weimer, Alexandros Karatzoglou, Quoc Viet Le, and Alex Smola. 2007. COFIRANK Maximum Margin Matrix Factorization for Collaborative Ranking. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS'07). 1593--1600. Google ScholarDigital Library
- Chao-YuanWu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and HowJing. 2017. Recurrent Recommender Networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM '17). ACM, New York, NY, USA, 495--503. Google ScholarDigital Library
Index Terms
- Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
Recommendations
Improved Recurrent Neural Networks for Session-based Recommendations
DLRS 2016: Proceedings of the 1st Workshop on Deep Learning for Recommender SystemsRecurrent neural networks (RNNs) were recently proposed for the session-based recommendation task. The models showed promising improvements over traditional recommendation approaches. In this work, we further study RNN-based models for session-based ...
News Session-Based Recommendations using Deep Neural Networks
DLRS 2018: Proceedings of the 3rd Workshop on Deep Learning for Recommender SystemsNews recommender systems are aimed to personalize users experiences and help them to discover relevant articles from a large and dynamic search space. Therefore, news domain is a challenging scenario for recommendations, due to its sparse user profiling,...
Combining User-Based and Session-Based Recommendations with Recurrent Neural Networks
Neural Information ProcessingAbstractRecommender systems generate recommendations based on user profiles, which consist of past interactions of users with items. When user profiles are not available, session-based recommendation can be used instead to make predictions based on ...
Comments