{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:03Z","timestamp":1750220283877,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,2,11]]},"DOI":"10.1145\/3488560.3501394","type":"proceedings-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T21:42:57Z","timestamp":1644961377000},"page":"1635-1637","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Modern Theoretical Tools for Understanding and Designing Next-generation Information Retrieval System"],"prefix":"10.1145","author":[{"given":"Da","family":"Xu","sequence":"first","affiliation":[{"name":"Walmart Labs, Sunnyvale, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanwei","family":"Ruan","sequence":"additional","affiliation":[{"name":"Instacart, San Francisco, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Tutorial on Deep Learning for Industrial AI: Challenges, New Methods and Best Practices. SIGKDD","author":"Chetan Gupta","year":"2020","unstructured":"Gupta Chetan and Farahat Ahmed. 2020. Tutorial on Deep Learning for Industrial AI: Challenges, New Methods and Best Practices. SIGKDD (2020)."},{"key":"e_1_3_2_1_2_1","volume-title":"Rethinking Pre-trained Embedding for Recommender Systems. Under Review","author":"Da Xu","year":"2021","unstructured":"Da Xu el al. 2021. Rethinking Pre-trained Embedding for Recommender Systems. Under Review (2021)."},{"key":"e_1_3_2_1_3_1","volume-title":"Tutorial on Interactive Information Retrieval with Bandit Feedback. SIGIR","author":"Huazheng Wang","year":"2021","unstructured":"Wang Huazheng, Jia Yiling, and Wang Hongning. 2021. Tutorial on Interactive Information Retrieval with Bandit Feedback. SIGIR (2021)."},{"key":"e_1_3_2_1_4_1","volume-title":"2nd International Workshop on Industrial Recommendation Systems","author":"Jie Cheng","year":"2021","unstructured":"Cheng Jie, Da Xu, Zigeng Wang, Lu Wang, and Wei Shen. 2021. Bidding via clustering ads intentions: an efficient search engine marketing system for e-commerce. 2nd International Workshop on Industrial Recommendation Systems (2021)."},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems. 4868--4878","author":"Jin Chi","year":"2018","unstructured":"Chi Jin, Zeyuan Allen-Zhu, Sebastien Bubeck, and Michael I Jordan. 2018. Is Q-learning provably efficient?. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 4868--4878."},{"key":"e_1_3_2_1_6_1","volume-title":"The do-calculus revisited. arXiv preprint arXiv:1210.4852","author":"Pearl Judea","year":"2012","unstructured":"Judea Pearl. 2012. The do-calculus revisited. arXiv preprint arXiv:1210.4852 (2012)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-control-053018-023825"},{"key":"e_1_3_2_1_8_1","volume-title":"Neural Collaborative Filtering vs. Matrix Factorization Revisited. arXiv preprint arXiv:2005.09683","author":"Rendle Steffen","year":"2020","unstructured":"Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020. Neural Collaborative Filtering vs. Matrix Factorization Revisited. arXiv preprint arXiv:2005.09683 (2020)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3058954"},{"key":"e_1_3_2_1_10_1","volume-title":"Tutorial on Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments. WSDM","author":"Somit Gupta","year":"2020","unstructured":"Gupta Somit, Shi Xiaolin, Dmitriev Pavel, and Fu Xin. 2020. Tutorial on Challenges, Best Practices and Pitfalls in Evaluating Results of Online Controlled Experiments. WSDM (2020)."},{"key":"e_1_3_2_1_11_1","volume-title":"Necati Cihan Camgoz, and Richard Bowden","author":"Vowels Matthew J","year":"2021","unstructured":"Matthew J Vowels, Necati Cihan Camgoz, and Richard Bowden. 2021. D'ya like DAGs? A Survey on Structure Learning and Causal Discovery. arXiv preprint arXiv:2103.02582 (2021)."},{"key":"e_1_3_2_1_12_1","first-page":"799","article-title":"a. Methods and apparatus for item substitution","volume":"16","author":"Xu Da","year":"2020","unstructured":"Da Xu, RUAN Chuanwei, Kamiya Motwani, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020 a. Methods and apparatus for item substitution. US Patent App. 16\/424,799.","journal-title":"US Patent App."},{"key":"e_1_3_2_1_13_1","volume-title":"Adversarial Counterfactual Learning and Evaluation for Recommender System. Advances in Neural Information Processing Systems","volume":"33","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020 b. Adversarial Counterfactual Learning and Evaluation for Recommender System. Advances in Neural Information Processing Systems , Vol. 33 (2020)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371778"},{"key":"e_1_3_2_1_15_1","volume-title":"Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives. In International Conference on Machine Learning . PMLR, 11514--11524","author":"Xu Da","year":"2021","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2021 a. Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives. In International Conference on Machine Learning . PMLR, 11514--11524."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441736"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467192"},{"key":"e_1_3_2_1_18_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Xu Da","year":"2021","unstructured":"Da Xu, Yuting Ye, and Chuanwei Ruan. 2021 d. Understanding the role of importance weighting for deep learning. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net."},{"key":"e_1_3_2_1_19_1","volume-title":"Towards Robust Off-policy Learning for Runtime Uncertainty. Proc","author":"Xu Da","year":"2022","unstructured":"Da Xu, Yuting Ye, Chuanwei Ruan, and Bo Yang. 2022. Towards Robust Off-policy Learning for Runtime Uncertainty. Proc. Association for the Advancement of Artificial Intelligence (2022)."},{"key":"e_1_3_2_1_20_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Xu Keyulu","year":"2021","unstructured":"Keyulu Xu, Mozhi Zhang, Jingling Li, Simon Shaolei Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2021 e. How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net."}],"event":{"name":"WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event AZ USA","acronym":"WSDM '22"},"container-title":["Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3501394","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3488560.3501394","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:20Z","timestamp":1750188680000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3488560.3501394"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":20,"alternative-id":["10.1145\/3488560.3501394","10.1145\/3488560"],"URL":"https:\/\/doi.org\/10.1145\/3488560.3501394","relation":{},"subject":[],"published":{"date-parts":[[2022,2,11]]},"assertion":[{"value":"2022-02-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}