{"type":"ScholarlyArticle","title":"Causal Embeddings for Recommendation: An Extended Abstract","authors":[{"name":"Flavian Vasile","slug":"flavian-vasile","given":"Flavian","family":"Vasile","affiliation":"Durham University , Durham , UK; Criteo AI Lab , Paris , France"},{"name":"Stephen Bonner","slug":"stephen-bonner","given":"Stephen","family":"Bonner","affiliation":"Criteo AI Lab , Paris , France"}],"abstract":"Recommendations are commonly used to modify\r\nuser’s natural behavior, for example, increasing\r\nproduct sales or the time spent on a website. This\r\nresults in a gap between the ultimate business ob-\r\njective and the classical setup where recommenda-\r\ntions are optimized to be coherent with past user be-\r\nhavior. To bridge this gap, we propose a new learn-\r\ning setup for recommendation that optimizes for the\r\nIncremental Treatment Effect (ITE) of the policy.\r\nWe show this is equivalent to learning to predict\r\nrecommendation outcomes under a fully random\r\nrecommendation policy and propose a new domain\r\nadaptation algorithm that learns from logged data\r\ncontaining outcomes from a biased recommenda-\r\ntion policy and predicts recommendation outcomes\r\naccording to random exposure. We compare our\r\nmethod against state-of-the-art factorization meth-\r\nods, in addition to new approaches of causal rec-\r\nommendation and show significant improvements.","doi":"10.24963/ijcai.2019/870","url":"https://ijcai.org/proceedings/2019/870","pdf":"https://ijcai.org/proceedings/2019/0870.pdf","markdown":"https://ijcai.org/proceedings/2019/870.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/870","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Best Sister Conferences","track":"Best Sister Conferences","number":870,"pages":{"first":6236,"last":6240},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Bonner and Vasile, 2018] Stephen Bonner and Flavian Vasile. Causal embeddings for recommendation. In Pro- ceedings of the 12th ACM Conference on Recommender Systems, pages 104-112. ACM, 2018.","title":"Causal embeddings for recommendation","authors":"Vasile Bonner, Stephen Bonner, Flavian Vasile","year":"2018","venue":"Proceedings of the 12th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Grbovic et al., 2015] Mihajlo Grbovic, Vladan Radosavlje- vic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, and Doug Sharp. E-commerce in your inbox: Product recommendations at scale. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pages 1809-1818, New York, NY, USA, 2015. ACM.","title":"E-commerce in your inbox: Product recommendations at scale","authors":"Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Jaikit Savla, Varun Bhagwan, Doug Sharp","year":"2015","venue":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Hidasi et al., 2015] Balázs Hidasi, Alexandros Karat- zoglou, Linas Baltrunas, and Domonkos Tikk. Session- based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939, 2015.","title":"Sessionbased recommendations with recurrent neural networks","authors":"Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, Domonkos Tikk","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hidasi et al., 2016] Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, and Domonkos Tikk. Paral- lel recurrent neural network architectures for feature-rich session-based recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 241-248. ACM, 2016.","title":"Parallel recurrent neural network architectures for feature-rich session-based recommendations","authors":"Balázs Hidasi, Massimo Quadrana, Alexandros Karatzoglou, Domonkos Tikk","year":"2016","venue":"Proceedings of the 10th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Joachims et al., 2018] Thorsten Joachims, Artem Grotov, Adith Swaminathan, and Maarten de Rijke. Deep learn- ing with logged bandit feedback. Proceedings of the Inter- national Conference on Learning Representations (ICLR), 2018.","title":"Deep learning with logged bandit feedback","authors":"Thorsten Joachims, Artem Grotov, Adith Swaminathan, Maarten De Rijke","year":"2018","venue":"Proceedings of the International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Johansson et al., 2016] Fredrik D Johansson, Uri Shalit, and David Sontag. Learning representations for counterfactual inference. arXiv preprint arXiv:1605.03661, 2016.","title":"Learning representations for counterfactual inference","authors":"Uri Fredrik D Johansson, David Shalit, Sontag","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Liang et al., 2016] Dawen Liang, Laurent Charlin, and David M Blei. Causal inference for recommendation. 2016. [","title":"Causal inference for recommendation","authors":"Dawen Liang, Laurent Charlin, David M Blei","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Pennington et al., 2014] Jeffrey Pennington, Richard Socher, and Christopher Manning. Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543, Doha, Qatar, 2014. Association for Computational Linguistics.","title":"Glove: Global Vectors for Word Representation","authors":"Jeffrey Pennington, Richard Socher, Christopher Manning","year":"2014","venue":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Rendle et al., 2009] Steffen Rendle, Christoph Freuden- thaler, Zeno Gantner, and Lars Schmidt-Thieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pages 452-461. AUAI Press, 2009.","title":"Bpr: Bayesian personalized ranking from implicit feedback","authors":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme","year":"2009","venue":"Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Rendle, 2010] Steffen Rendle. Factorization machines. In Data Mining (ICDM), 2010 IEEE 10th International Con- ference on, pages 995-1000. IEEE, 2010.","title":"Factorization machines","authors":"Steffen Rendle","year":"2010","venue":"IEEE 10th International Conference on","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Rosenfeld et al., 2016] Nir Rosenfeld, Yishay Mansour, and Elad Yom-Tov. Predicting counterfactuals from large his- torical data and small randomized trials. arXiv preprint arXiv:1610.07667, 2016.","title":"Predicting counterfactuals from large historical data and small randomized trials","authors":"Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Rubin, 1974] Donald B Rubin. Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of educational Psychology, 66(5):688, 1974.","title":"Estimating causal effects of treatments in randomized and nonrandomized studies","authors":"Donald B Rubin","year":"1974","venue":"Journal of educational Psychology","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Schnabel et al., 2016] Tobias Schnabel, Adith Swami- nathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. Recommendations as treatments: Debiasing learning and evaluation. In Proceedings of the 33rd International Conference on International Conference on Machine Learning -Volume 48, ICML'16, pages 1670-1679, 2016.","title":"Recommendations as treatments: Debiasing learning and evaluation","authors":"Schnabel","year":"2016","venue":"Proceedings of the 33rd International Conference on International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Shalit et al., 2017] Uri Shalit, Fredrik D Johansson, and David Sontag. Estimating individual treatment effect: generalization bounds and algorithms. In Proceedings of the 34th International Conference on Machine Learning- Volume 70, pages 3076-3085. JMLR. org, 2017.","title":"Estimating individual treatment effect: generalization bounds and algorithms","authors":"Shalit","year":"2017","venue":"Proceedings of the 34th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Swaminathan and Joachims, 2015] Adith Swaminathan and Thorsten Joachims. Batch learning from logged bandit feedback through counterfactual risk minimization. Jour- nal of Machine Learning Research, 16:1731-1755, 2015.","title":"Batch learning from logged bandit feedback through counterfactual risk minimization","authors":"Joachims Swaminathan, Adith Swaminathan, Thorsten Joachims","year":"2015","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Vasile et al., 2016] Flavian Vasile, Elena Smirnova, and Alexis Conneau. Meta-prod2vec: Product embeddings us- ing side-information for recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems, pages 225-232. ACM, 2016.","title":"Meta-prod2vec: Product embeddings using side-information for recommendation","authors":"Flavian Vasile, Elena Smirnova, Alexis Conneau","year":"2016","venue":"Proceedings of the 10th ACM Conference on Recommender Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}