Shashank Gupta
ABSTRACT
Variational autoencoders (VAEs) are the state-of-the-art model for recommendation with implicit feedback signals. Unfortunately, implicit feedback suffers from selection bias, e.g., popularity bias, position bias, etc., and as a result, training from such signals produces biased recommendation models. Existing methods for debiasing the learning process have not been applied in a generative setting. We address this gap by introducing an inverse propensity scoring (IPS) based method for training VAEs from implicit feedback data in an unbiased way. Our IPS-based estimator for the VAE training objective, VAE-IPS, is provably unbiased w.r.t. selection bias. Our experimental results show that the proposed VAE-IPS model reaches significantly higher performance than existing baselines. Our contributions enable practitioners to combine state-of-the-art VAE recommendation techniques with the advantages of bias mitigation for implicit feedback.
- Aman Agarwal, Xuanhui Wang, Cheng Li, Michael Bendersky, and Marc Najork. 2019. Addressing Trust Bias for Unbiased Learning-to-rank. In The World Wide Web Conference. 4--14.Google Scholar
- Roc'io Ca namares and Pablo Castells. 2018. Should I Follow the Crowd? A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 415--424.Google Scholar
- Laurent Charlin, Rajesh Ranganath, James McInerney, and David M. Blei. 2015. Dynamic Poisson Factorization. In Proceedings of the 9th ACM Conference on Recommender Systems. 155--162.Google Scholar
- Aleksandr Chuklin, Ilya Markov, and Maarten de Rijke. 2015. Click Models for Web Search. Morgan & Claypool Publishers.Google Scholar
- Nick Craswell, Onno Zoeter, Michael Taylor, and Bill Ramsey. 2008. An Experimental Comparison of Click Position-bias Models. In Proceedings of the 2008 international conference on web search and data mining. 87--94.Google ScholarDigital Library
- Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are We really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches. In Proceedings of the 13th ACM Conference on Recommender Systems. 101--109.Google ScholarDigital Library
- Marco Ferrante, Nicola Ferro, and Norbert Fuhr. 2021. Towards Meaningful Statements in IR Evaluation: Mapping Evaluation Measures to Interval Scales. IEEE Access, Vol. 9 (2021), 136182--136216.Google ScholarCross Ref
- Prem Gopalan, Jake M. Hofman, and David M. Blei. 2015. Scalable Recommendation with Hierarchical Poisson Factorization. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. 326--335.Google Scholar
- Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, and Harrie Oosterhuis. 2023 a. Recent Advances in the Foundations and Applications of Unbiased Learning to Rank. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval.Google ScholarDigital Library
- Shashank Gupta, Harrie Oosterhuis, and Maarten de Rijke. 2023 b. Safe Deployment for Counterfactual Learning to Rank with Exposure-Based Risk Minimization. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval.Google ScholarDigital Library
- F. Maxwell Harper and Joseph A. Konstan. 2015. The Movielens datasets: History and Context. ACM Transactions on Interactive Intelligent Systems, Vol. 5, 4 (2015), 1--19.Google ScholarDigital Library
- Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web. 173--182.Google ScholarDigital Library
- Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 549--558.Google ScholarDigital Library
- Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative Filtering for Implicit Feedback Datasets. In Eighth IEEE International Conference on Data Mining. IEEE, 263--272.Google Scholar
- Jin Huang, Harrie Oosterhuis, Maarten de Rijke, and Herke van Hoof. 2020. Keeping Dataset Biases Out of the Simulation: A Debiased Simulator for Reinforcement Learning Based Recommender Systems. In Fourteenth ACM Conference on Recommender Systems. 190--199.Google ScholarDigital Library
- Ilija Ilievski and Sujoy Roy. 2013. Personalized News Recommendation Based on Implicit Feedback. In Proceedings of the 2013 International News Recommender Systems Workshop and Challenge. 10--15.Google ScholarDigital Library
- Dietmar Jannach, Lukas Lerche, and Markus Zanker. 2018. Recommending Based on Implicit Feedback. In Social Information Access. Springer, 510--569.Google Scholar
- Thorsten Joachims, Adith Swaminathan, and Tobias Schnabel. 2017. Unbiased Learning-to-rank with Biased Feedback. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. 781--789.Google ScholarDigital Library
- Diederik P. Kingma and Max Welling. 2014. Auto-encoding Variational Bayes. In International Conference on Learning Representations.Google Scholar
- Jae-woong Lee, Seongmin Park, and Jongwuk Lee. 2021. Dual Unbiased Recommender Learning for Implicit Feedback. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1647--1651.Google Scholar
- Dawen Liang, Rahul G. Krishnan, Matthew D. Hoffman, and Tony Jebara. 2018. Variational Autoencoders for Collaborative Filtering. In Proceedings of the 2018 world wide web conference. 689--698.Google ScholarDigital Library
- Roderick J.A. Little and Donald B. Rubin. 2002. Statistical Analysis with Missing Data. John Wiley & Sons.Google Scholar
- Benjamin M. Marlin and Richard S. Zemel. 2009. Collaborative Prediction and Ranking with Non-random Missing Data. In Proceedings of the third ACM conference on Recommender systems. 5--12.Google Scholar
- Benjamin M Marlin, Richard S Zemel, Sam Roweis, and Malcolm Slaney. 2007. Collaborative filtering and the missing at random assumption. In Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence. 267--275.Google ScholarDigital Library
- Harrie Oosterhuis. 2020. Learning from User Interactions with Rankings: A Unification of the Field. Ph.,D. Dissertation. Informatics Institute, University of Amsterdam.Google Scholar
- Harrie Oosterhuis. 2023. Doubly Robust Estimation for Correcting Position Bias in Click Feedback for Unbiased Learning to Rank. ACM Transactions on Information Systems, Vol. 41, 3 (2023), 1--33.Google ScholarDigital Library
- Harrie Oosterhuis and Maarten de Rijke. 2021. Unifying Online and Counterfactual Learning to Rank: A Novel Counterfactual Estimator that Effectively Utilizes Online Interventions. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 463--471.Google ScholarDigital Library
- Harrie Oosterhuis, Rolf Jagerman, and Maarten de Rijke. 2020. Unbiased Learning to Rank: Counterfactual and Online Approaches. In Companion Proceedings of the Web Conference 2020. 299--300.Google ScholarDigital Library
- Bruno Pradel, Nicolas Usunier, and Patrick Gallinari. 2012. Ranking with Non-random Missing Ratings: Influence of Popularity and Positivity on Evaluation Metrics. In Proceedings of the sixth ACM conference on Recommender systems. 147--154.Google ScholarDigital Library
- Sanjay Purushotham, Yan Liu, and C-C Jay Kuo. 2012. Collaborative topic regression with social matrix factorization for recommendation systems. In Proceedings of the 29th International Conference on Machine Learning. 691--698.Google Scholar
- Yuta Saito. 2020a. Asymmetric Tri-training for Debiasing Missing-not-at-random Explicit Feedback. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 309--318.Google ScholarDigital Library
- Yuta Saito. 2020b. Unbiased Pairwise Learning from Biased Implicit Feedback. In Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. 5--12.Google ScholarDigital Library
- Yuta Saito and Masahiro Nomura. 2022. Towards Resolving Propensity Contradiction in Offline Recommender Learning. In Proceedings of the 31st International Joint Conference on Artificial Intelligence. 2211--2217.Google ScholarCross Ref
- Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. 2020. Unbiased Recommender Learning from Missing-not-at-random Implicit Feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining. 501--509.Google ScholarDigital Library
- Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as Treatments: Debiasing Learning and Evaluation. In International Conference on Machine Learning. PMLR, 1670--1679.Google Scholar
- Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, and Sergey I Nikolenko. 2020. RecVAE: A New Variational Autoencoder for Top-n Recommendations with Implicit Feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining. 528--536.Google ScholarDigital Library
- Harald Steck. 2011. Item Popularity and Recommendation Accuracy. In Proceedings of the fifth ACM conference on Recommender systems. 125--132.Google ScholarDigital Library
- Alex Strehl, John Langford, Lihong Li, and Sham M. Kakade. 2010. Learning from Logged Implicit Exploration Data. In Advances in Neural Information Processing Systems, J. Lafferty, C. Williams, J. Shawe-Taylor, R. Zemel, and A. Culotta (Eds.), Vol. 23. Curran Associates, Inc.Google Scholar
- Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Advances in artificial intelligence, Vol. 2009 (2009).Google Scholar
- Adith Swaminathan and Thorsten Joachims. 2015. Batch Learning from Logged Bandit Feedback through Counterfactual Risk Minimization. The Journal of Machine Learning Research, Vol. 16, 1 (2015), 1731--1755.Google ScholarDigital Library
- Quoc-Tuan Truong, Aghiles Salah, and Hady W. Lauw. 2021. Bilateral Variational Autoencoder for Collaborative Filtering. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 292--300.Google Scholar
- Ali Vardasbi, Harrie Oosterhuis, and Maarten de Rijke. 2020. When Inverse Propensity Scoring does not Work: Affine Corrections for Unbiased Learning to Rank. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 1475--1484.Google ScholarDigital Library
- Xuanhui Wang, Nadav Golbandi, Michael Bendersky, Donald Metzler, and Marc Najork. 2018. Position Bias Estimation for Unbiased Learning to Rank in Personal Search. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. 610--618.Google ScholarDigital Library
- Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi. 2019. Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random. In International Conference on Machine Learning. PMLR, 6638--6647.Google Scholar
- Sheng Zhang, Weihong Wang, James Ford, and Fillia Makedon. 2006. Learning from Incomplete Ratings Using Non-negative Matrix Factorization. In Proceedings of the 2006 SIAM international conference on data mining. SIAM, 549--553.Google ScholarCross Ref
- Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Computing Surveys (CSUR), Vol. 52, 1 (2019), 1--38.Google ScholarDigital Library
Index Terms
- A Deep Generative Recommendation Method for Unbiased Learning from Implicit Feedback
Recommendations
A latent pairwise preference learning approach for recommendation from implicit feedback
CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge managementMost of the current recommender systems heavily rely on explicit user feedback such as ratings on items to model users' interests. However, in many applications, it is very hard to collect the explicit feedback, while implicit feedback such as user ...
Unifying explicit and implicit feedback for collaborative filtering
CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge managementMost collaborative filtering algorithms are based on certain statistical models of user interests built from either explicit feedback (eg: ratings, votes) or implicit feedback (eg: clicks, purchases). Explicit feedbacks are more precise but more ...
Comparison of implicit and explicit feedback from an online music recommendation service
HetRec '10: Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender SystemsExplicit and implicit feedback exhibits different characteristics of users' preferences with both pros and cons. However, a combination of these two types of feedback provides another paradigm for recommender systems (RS). Their combination in a user ...
Comments