Saito, Y., Aihara, S., Matsutani, M., Narita, Y. Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation. arXiv preprint arXiv:2008.07146 (2020)
Covington, P., Jay, A., Sargin, E. Deep neural networks for YouTube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems (RecSys ’16). Association for Computing Machinery, New York, NY, USA, pp. 191–198 (2016)
Google Scholar
Mehrotra, R., McInerney, J., Bouchard, H., Lalmas, M., Diaz, F. Towards a fair marketplace: counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18). Association for Computing Machinery, New York, NY, USA, pp. 2243–2251 (2018)
Google Scholar
Chen, J., Dong, H., Wang, X., Feng, F., Wang, M., He, X.: Bias and debias in recommender system: a survey and future directions. ACM T. Inform. Sys. 41(3), 1–39 (2023)
MATH
Google Scholar
Baeza-Yates, R.: Bias on the web. Commun. ACM 61(6), 54–61 (2018). https://doi.org/10.1145/3209581
Chaney, A.J.B., Stewart, B.M., Engelhardt, B.E.: How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 224-232. ACM (2018)
Google Scholar
Rendle, S.: Factorization machines. In: ICDM’10, pp. 995-1000 (2010)
Google Scholar
Saito, Y., Yaginuma, Y., Nishino, Y., Sakata, H., Nakata, K.: Unbiased recommender learning from missing-not-at-random implicit feedback. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 501–509 (2020)
Google Scholar
Saito, Y.: Unbiased pairwise learning from biased implicit feedback. In: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval (ICTIR ’20). Association for Computing Machinery, New York, NY, USA, pp. 5–12 (2020)
Google Scholar
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Comput. 8, 30–37 (2009)
MATH
Google Scholar
Yang, L., Cui, Y., Xuan, Y., Wang, C., Belongie, S., Estrin, D.: Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. In: Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18). Association for Computing Machinery, New York, NY, USA, pp. 279–287 (2018)
Google Scholar
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the fourth ACM conference on Recommender systems (RecSys ’10). Association for Computing Machinery, New York, NY, USA, pp. 39–46 (2010)
Google Scholar
Bernardi, L., Mavridis, T., Estevez, P.: 150 successful machine learning models: 6 lessons learned at booking.com. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19). Association for Computing Machinery, New York, NY, USA, pp. 1743–1751 (2019)
Google Scholar
Marlin, M., Zemel, S., Roweis, S., Slaney, M.: Collaborative filtering and the missing at random assumption. In: Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI’07). AUAI Press, Arlington, Virginia, USA, pp. 267–275 (2007)
Google Scholar
Schnabel, T., Swaminathan, A., Singh, A., Chandak, N., Joachims, T.: Recommendations as treatments: debiasing learning and evaluation. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 (ICML’16). JMLR.org, pp. 1670–1679 (2016)
Google Scholar
Ai, Q., Bi, K., Luo, C., Guo, J., Croft, W.B.: Unbiased learning to rank with unbiased propensity estimation. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR ’18). Association for Computing Machinery, New York, NY, USA, pp. 385–394 (2018)
Google Scholar
Dudík, M., Langford, J., Li, L.: Doubly robust policy evaluation and learning. In: Proceedings of the 28th International Conference on International Conference on Machine Learning (ICML’11). Omnipress, Madison, WI, USA, pp. 1097–1104 (2011)
Google Scholar
Saito, Y., Joachims, T.: Off-policy evaluation for large action spaces via embeddings. In: International Conference on Machine Learning, pp. 19089–19122. PMLR (2022)
Google Scholar
Li, S., Abbasi-Yadkori, Y., Kveton, B., Muthukrishnan, S., Vinay, V., Wen, Z.: Offline evaluation of ranking policies with click models. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18). Association for Computing Machinery, New York, NY, USA, pp. 1685–1694 (2018)
Google Scholar
Gao, C., et al.: KuaiRec: A Fully-observed Dataset and Insights for Evaluating Recommender Systems. arXiv preprint arXiv:2202.10842 (2022)
Google Scholar
Saito, Y.: Doubly robust estimator for ranking metrics with post-click conversions. In: Proceedings of the 14th ACM Conference on Recommender Systems (RecSys ’20). Association for Computing Machinery, New York, NY, USA, pp. 92–100 (2020)
Google Scholar
