{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:50Z","timestamp":1750309550861,"version":"3.41.0"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62141607 and 62425206"],"award-info":[{"award-number":["62141607 and 62425206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>Recommendation systems are frequently challenged by pervasive biases in the training set that can compromise model effectiveness. To address this issue, various debiasing techniques have been developed to eliminate biases and produce debiased models. However, when encountering varying test environments, some data patterns manifested by the training data could be beneficial to the model\u2019s performance. Completely removing biases may overlook the beneficial data patterns and consequently diminish recommendation accuracy. Thus, it is crucial to carefully integrate certain biases to optimize performance, while the ideal level of bias integration is highly dependent on the test environment. Moreover, these systems operate in dynamic scenarios where the test environments could vary, necessitating an adaptive integration strategy customized to the environment. Our research establishes that discrepancies in predictions of models can guide the selection of the most fitting model for specific situations. Building on this understanding, we present AdaptSel, a pioneering method for the adaptive selection of the superior model during the testing phase. Empirical evaluations substantiate the foundational assumptions of AdaptSel, accentuating its effectiveness in adaptively selecting the most suitable model for varying test environments.<\/jats:p>","DOI":"10.1145\/3706637","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T14:52:18Z","timestamp":1733410338000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AdaptSel: Adaptive Selection of Biased and Debiased Recommendation Models for Varying Test Environments"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1180-6395","authenticated-orcid":false,"given":"Zimu","family":"Wang","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6000-6936","authenticated-orcid":false,"given":"Hao","family":"Zou","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9159-1752","authenticated-orcid":false,"given":"Jiashuo","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7131-7290","authenticated-orcid":false,"given":"Jiayun","family":"Wu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1747-0687","authenticated-orcid":false,"given":"Pengfei","family":"Tian","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1536-1179","authenticated-orcid":false,"given":"Yue","family":"He","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2957-8511","authenticated-orcid":false,"given":"Peng","family":"Cui","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3209986"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240360"},{"key":"e_1_3_2_4_2","unstructured":"Tiffany Tianhui Cai Hongseok Namkoong and Steve Yadlowsky. 2023. Diagnosing model performance under distribution shift. arXiv:2303.02011. Retrieved from https:\/\/arxiv.org\/abs\/2303.02011"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462919"},{"issue":"3","key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3564284","article-title":"Bias and debias in recommender system: A survey and future directions","volume":"41","author":"Chen Jiawei","year":"2023","unstructured":"Jiawei Chen, Hande Dong, Xiang Wang, Fuli Feng, Meng Wang, and Xiangnan He. 2023. Bias and debias in recommender system: A survey and future directions. ACM Transactions on Information Systems 41, 3 (2023), 1\u201339.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3473321"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539270"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532002"},{"key":"e_1_3_2_10_2","unstructured":"Chen Gao Yu Zheng Wenjie Wang Fuli Feng Xiangnan He and Yong Li. 2022. Causal inference in recommender systems: A survey and future directions. arXiv:2208.12397. Retrieved from https:\/\/arxiv.org\/abs\/2208.12397"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462917"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_13_2","first-page":"410","volume-title":"Proceedings of the ACM Web Conference","author":"He Yue","year":"2022","unstructured":"Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui, and Yong Jiang. 2022. Causpref: Causal preference learning for out-of-distribution recommendation. In Proceedings of the ACM Web Conference, 410\u2013421."},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eij.2015.06.005"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41870-018-0138-8"},{"key":"e_1_3_2_16_2","unstructured":"Haoxuan Li Yan Lyu Chunyuan Zheng and Peng Wu. 2022. TDR-CL: Targeted doubly robust collaborative learning for debiased recommendations. arXiv:2203.10258. Retrieved from https:\/\/arxiv.org\/abs\/2203.10258"},{"key":"e_1_3_2_17_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Li Haoxuan","year":"2023","unstructured":"Haoxuan Li, Chunyuan Zheng, and Peng Wu. 2023. StableDR: Stabilized doubly robust learning for recommendation on data missing not at random. In Proceedings of the 11th International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=3VO1y5N7K1H"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3474263"},{"key":"e_1_3_2_20_2","article-title":"On the need for a language describing distribution shifts: Illustrations on tabular datasets","volume":"36","author":"Liu Jiashuo","year":"2024","unstructured":"Jiashuo Liu, Tianyu Wang, Peng Cui, and Hongseok Namkoong. 2024. On the need for a language describing distribution shifts: Illustrations on tabular datasets. Advances in Neural Information Processing Systems 36 (2024).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_21_2","unstructured":"Jiashuo Liu Jiayun Wu Bo Li and Peng Cui. 2023. Predictive heterogeneity: Measures and applications. arXiv:2304.00305. Retrieved from https:\/\/arxiv.org\/abs\/2304.00305"},{"key":"e_1_3_2_22_2","unstructured":"Huishi Luo Fuzhen Zhuang Ruobing Xie Hengshu Zhu and Deqing Wang. 2023. A survey on causal inference for recommendation. arXiv:2303.11666. Retrieved from https:\/\/arxiv.org\/abs\/2303.11666"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401100"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401114"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3383313.3412262"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371783"},{"key":"e_1_3_2_27_2","first-page":"1670","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Schnabel Tobias","year":"2016","unstructured":"Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, and Thorsten Joachims. 2016. Recommendations as treatments: Debiasing learning and evaluation. In Proceedings of the International Conference on Machine Learning. PMLR, 1670\u20131679."},{"issue":"5","key":"e_1_3_2_28_2","first-page":"1989","article-title":"A survey of recommendation system: Research challenges","volume":"4","author":"Sharma Lalita","year":"2013","unstructured":"Lalita Sharma and Anju Gera. 2013. A survey of recommendation system: Research challenges. International Journal of Engineering Trends and Technology 4, 5 (2013), 1989\u20131992.","journal-title":"International Journal of Engineering Trends and Technology"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835895"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507160"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.183.6.01831539"},{"key":"e_1_3_2_33_2","unstructured":"Naftali Tishby Fernando C. Pereira and William Bialek. 2000. The information bottleneck method. arXiv:physics\/ 0004057. Retrieved from https:\/\/arxiv.org\/abs\/0004057"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3490099.3511108"},{"key":"e_1_3_2_35_2","first-page":"3562","volume-title":"Proceedings of the ACM Web Conference","author":"Wang Wenjie","year":"2022","unstructured":"Wenjie Wang, Xinyu Lin, Fuli Feng, Xiangnan He, Min Lin, and Tat-Seng Chua. 2022b. Causal representation learning for out-of-distribution recommendation. In Proceedings of the ACM Web Conference, 3562\u20133571."},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3594245"},{"key":"e_1_3_2_37_2","first-page":"6638","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wang Xiaojie","year":"2019","unstructured":"Xiaojie Wang, Rui Zhang, Yu Sun, and Jianzhong Qi. 2019. Doubly robust joint learning for recommendation on data missing not at random. In Proceedings of the International Conference on Machine Learning. PMLR, 6638\u20136647."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441799"},{"key":"e_1_3_2_39_2","volume-title":"Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS)","author":"Wang Zifeng","year":"2020","unstructured":"Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E Kuruoglu, and Yefeng Zheng. 2020. Information theoretic counterfactual learning from missing-not-at-random feedback. In Proceedings of the International Conference on Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539439"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467289"},{"issue":"5","key":"e_1_3_2_42_2","first-page":"4425","article-title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-Rich recommendation","volume":"35","author":"Wu Le","year":"2022","unstructured":"Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-Rich recommendation. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 4425\u20134445.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2003.1238383"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240355"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583361"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401321"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3158369"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462875"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3218994"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449788"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467376"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706637","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706637","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:46Z","timestamp":1750295926000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,13]]},"references-count":50,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2,28]]}},"alternative-id":["10.1145\/3706637"],"URL":"https:\/\/doi.org\/10.1145\/3706637","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2025,1,13]]},"assertion":[{"value":"2024-07-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-01-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}