Maksims Volkovs
ABSTRACT
Most online activity happens in the context of a session; to enable better user experience many online platforms aim to dynamically refine their recommendations as sessions progress. A popular approach is to continuously re-rank recommendations based on current session activity and past session logs. This motivates the 2019 ACM RecSys Challenge organised by Trivago. Using the session log dataset released by Trivago, the challenge aims to benchmark models for in-session re-ranking of hotel recommendations. In this paper we present our approach to this challenge where we first contextualize sessions in a global and local manner, and then train gradient boosting and deep learning models for re-ranking. Our team achieved 2nd place out of over 570 teams, with less than 0.3% relative difference in Mean Reciprocal Rank from the 1st place team. Code for our approach can be found here: https://github.com/layer6ai-labs/RecSys2019
- Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). ACM, New York, NY, USA, 785--794. Google Scholar
Digital Library
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv.1810.04805 (2018).Google Scholar
- Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, and Joaquin Quiñonero Candela. 2014. Practical Lessons from Predicting Clicks on Ads at Facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising (ADKDD'14). ACM, New York, NY, USA, Article 5, 9 pages. Google ScholarDigital Library
- Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).Google Scholar
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining. Ieee, 263--272.Google ScholarDigital Library
- Peter Knees, Yashar Deldjoo, Farshad Bakhshandegan Moghaddam, Jens Adamczak, Gerard-Paul Leyson, and Philipp Monreal. 2019. RecSys Challenge 2019: Session-based Hotel Recommendations. In Proceedings of the Thirteenth ACM Conference on Recommender Systems (RecSys '19). ACM, New York, NY, USA, 2. Google ScholarDigital Library
- Pablo Loyola, Chen Liu, and Yu Hirate. 2017. Modeling user session and intent with an attention-based encoder-decoder architecture. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 147--151.Google ScholarDigital Library
- Massimo Quadrana, Alexandras Karatzoglou, Balázs Hidasi, and Paolo Cremonesi. 2017. Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the Eleventh ACM Conference on Recommender Systems. ACM, 130--137.Google ScholarDigital Library
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 5998--6008. http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdfGoogle ScholarDigital Library
- Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019. XLNet: Generalized Autoregressive Pretraining for Language Understanding. arXiv preprint arXiv:1906.08237 (2019).Google Scholar
- I.K. Yeo and R.A. Johnson. 2000. A New Family of Power Transformations to Improve Normality or Symmetry. Biometrika 87, 4 (2000), 954--959. http://www.jstor.org/stable/2673623Google ScholarCross Ref
- Haochao Ying, Fuzhen Zhuang, Fuzheng Zhang, Yanchi Liu, Guandong Xu, Xing Xie, Hui Xiong, and Jian Wu. 2018. Sequential recommender system based on hierarchical attention networks. In the 27th International Joint Conference on Artificial Intelligence.Google ScholarDigital Library
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