Abstract
Session-based recommender systems have gained attention recently due to their potential for providing real-time personalized recommendations with high recall, especially when compared to traditional methods like matrix factorization and item-based collaborative filtering. Two recent methods are Short-Term Attention/Memory Priority Model for Session-based Recommendation (STAMP) and Neural Attentive Session-based Recommendation (NARM). However, when we applied these two methods to the similar-item recommendation dataset of Zalando, they did not outperform a simple collaborative filtering baseline.
Aiming for improving similar-item recommendation, in this work we propose to re-rank a list of generated candidates, by employing the user session information encoded by an attention network. We confirm the efficacy of this strategy when using a novel Candidate Rank Embedding that encodes the global ranking information of each candidate in the re-ranking process. Offline and online experiments show significant improvements over the baseline in terms of recall and MRR, as well as improvements in click-through rate. Additionally, we evaluate the potential of this method on the next click prediction problem, where, when applied to STAMP and NARM, it improves recall and MRR on two publicly available real-world datasets.
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References
Aiolli F (2013) A preliminary study on a recommender system for the Million Songs Dataset Challenge. In: CEUR Workshop Proceedings. http://ceur-ws.org/Vol-964/paper12.pdf
Covington P, Adams J, Sargin E (2016) Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys’16. ACM, New York, pp 191–198. https://doi.org/10.1145/2959100.2959190
Devooght R, Bersini H (2016) Collaborative filtering with recurrent neural networks. CoRR abs/1608.07400. http://arxiv.org/abs/1608.07400
Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Teh YW, Titterington M (eds) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, vol 9, 13–15 May 2010. PMLR, Chia Laguna Resort, Sardinia, pp 249–256. http://proceedings.mlr.press/v9/glorot10a.html
Hidasi B, Karatzoglou A (2018) Recurrent neural networks with top-k gains for session-based recommendations. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM’18. ACM, New York, pp 843–852. https://doi.org/10.1145/3269206.3271761
Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2015) Session-based recommendations with recurrent neural networks. CoRR abs/1511.06939. http://arxiv.org/abs/1511.06939
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR abs/1412.6980. http://arxiv.org/abs/1412.6980
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37. https://doi.org/10.1109/MC.2009.263
Li J, Ren P, Chen Z, Ren Z, Lian T, Ma J (2017) Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM’17. ACM, New York, pp 1419–1428. https://doi.org/10.1145/3132847.3132926
Linden G, Smith B, York J (2003) Amazon.com recommendations item-to-item collaborative filtering. IEEE Internet Comput 1(February):76–80. http://www.academia.edu/download/33248546/Amazon-Recommendations.pdf
Liu Q, Zeng Y, Mokhosi R, Zhang H (2018) STAMP: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’18. ACM, New York, pp 1831–1839. https://doi.org/10.1145/3219819.3219950
Ludewig M, Jannach D (2018) Evaluation of session-based recommendation algorithms. CoRR abs/1803.09587. http://arxiv.org/abs/1803.09587
Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI’09. AUAI Press, Arlington, pp 452–461. http://dl.acm.org/citation.cfm?id=1795114.1795167
Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW’10. ACM, New York, pp 811–820. https://doi.org/10.1145/1772690.1772773
Rubtsov V, Kamenshchikov M, Valyaev I, Leksin V, Ignatov DI (2018) A hybrid two-stage recommender system for automatic playlist continuation. In: Proceedings of the ACM Recommender Systems Challenge 2018. RecSys Challenge’18. ACM, New York, pp 16:1–16:4. https://doi.org/10.1145/3267471.3267488
de Souza Pereira Moreira G, Ferreira F, da Cunha AM (2018) News session-based recommendations using deep neural networks. In: Proceedings of the 3rd Workshop on Deep Learning for Recommender Systems, DLRS’18. ACM, New York, pp 15–23. https://doi.org/10.1145/3270323.3270328
Tan YK, Xu X, Liu Y (2016) Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS’16. ACM, New York, pp 17–22. https://doi.org/10.1145/2988450.2988452
Wu JC, Rodríguez JAS, Pampín HJC (2019) Session-based complementary fashion recommendations. In: Workshop on Recommender Systems in Fashion
Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T (2019) Session-based recommendation with graph neural networks. In: Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, vol 33, pp 346–353. http://arxiv.org/abs/1811.00855
Yu F, Liu Q, Wu S, Wang L, Tan T (2016) A dynamic recurrent model for next basket recommendation. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR’16. ACM, New York, pp 729–732. https://doi.org/10.1145/2911451.2914683
Acknowledgements
The authors are immensely grateful to Alan Akbik, Andrea Briceno, Humberto Corona, Antonino Freno, Zeno Gantner, Francis Gonzalez, Romain Guigoures, Sebastian Heinz, Bowen Li, Max Moeller, Roberto Roverso, Reza Shirvany, Julie Sanchez, Hao Su, Lina Weichbrodt, and Nana Yamazaki for their support, revisions, suggestions, ideas and comments that greatly helped to improve the quality of this work.
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Sánchez Rodríguez, J.A., Wu, JC., Khandwawala, M. (2020). Two-Stage Session-Based Recommendations with Candidate Rank Embeddings. In: Dokoohaki, N. (eds) Fashion Recommender Systems. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-55218-3_3
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