{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T01:11:30Z","timestamp":1777338690287,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819755547","type":"print"},{"value":"9789819755554","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-5555-4_11","type":"book-chapter","created":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T05:39:45Z","timestamp":1736573985000},"page":"164-179","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Counterfactual Debasing for Multi-behavior Recommendations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1206-2260","authenticated-orcid":false,"given":"Sirui","family":"Huang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8308-9551","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3643-3353","authenticated-orcid":false,"given":"Xiangmeng","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6376-9667","authenticated-orcid":false,"given":"Dianer","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-471X","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,12]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","unstructured":"Cheng, Z., Han, S., Liu, F., Zhu, L., Gao, Z., Peng, Y.: Multi-behavior recommendation with cascading graph convolution networks. In: Proceedings of the ACM Web Conference 2023. p. 1181-1189. WWW \u201923, Association for Computing Machinery, New York, NY, USA (2023). https:\/\/doi.org\/10.1145\/3543507.3583439, https:\/\/doi.org\/10.1145\/3543507.3583439","DOI":"10.1145\/3543507.3583439"},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Jin, B., Gao, C., He, X., Jin, D., Li, Y.: Multi-behavior recommendation with graph convolutional networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 659\u2013668 (2020)","DOI":"10.1145\/3397271.3401072"},{"key":"11_CR3","doi-asserted-by":"publisher","unstructured":"Li, Q., Wang, X., Wang, Z., Xu, G.: Be causal: De-biasing social network confounding in recommendation. ACM Trans. Knowl. Discov. Data 17(1) (feb 2023). https:\/\/doi.org\/10.1145\/3533725, https:\/\/doi.org\/10.1145\/3533725","DOI":"10.1145\/3533725"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Li, Q., Ma, H., Zhang, R., Jin, W., Li, Z.: Dual-view co-contrastive learning for multi-behavior recommendation. Applied Intelligence pp. 1\u201318 (2023)","DOI":"10.1016\/j.asoc.2023.110523"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Li, Y., Sun, X., Chen, H., Zhang, S., Yang, Y., Xu, G.: Attention is not the only choice: Counterfactual reasoning for path-based explainable recommendation. arXiv preprint arXiv:2401.05744 (2024)","DOI":"10.1109\/TKDE.2024.3373608"},{"key":"11_CR6","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality: Models","author":"J Pearl","year":"2009","unstructured":"Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press, USA, 2nd edn. (2009)","edition":"2"},{"key":"11_CR7","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. p. 452-461. AUAI Press, Arlington, Virginia, USA (2009)"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den\u00a0Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings 15. pp. 593\u2013607. Springer (2018)","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"11_CR9","doi-asserted-by":"publisher","unstructured":"Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. p. 1441-1450. CIKM \u201919, Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3357384.3357895, https:\/\/doi.org\/10.1145\/3357384.3357895","DOI":"10.1145\/3357384.3357895"},{"key":"11_CR10","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"issue":"2","key":"11_CR11","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1038\/s42256-023-00611-x","volume":"5","author":"A Vlontzos","year":"2023","unstructured":"Vlontzos, A., Kainz, B., Gilligan-Lee, C.M.: Estimating categorical counterfactuals via deep twin networks. Nature Machine Intelligence 5(2), 159\u2013168 (2023)","journal-title":"Nature Machine Intelligence"},{"key":"11_CR12","doi-asserted-by":"publisher","unstructured":"Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. p. 165-174. SIGIR\u201919, Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3331184.3331267, https:\/\/doi.org\/10.1145\/3331184.3331267","DOI":"10.1145\/3331184.3331267"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, Q., Yu, D., Li, Q., Xu, G.: Reinforced path reasoning for counterfactual explainable recommendation. IEEE Transactions on Knowledge and Data Engineering (2024)","DOI":"10.1109\/TKDE.2024.3354077"},{"key":"11_CR14","doi-asserted-by":"publisher","unstructured":"Wang, Z., Shen, S., Wang, Z., Chen, B., Chen, X., Wen, J.R.: Unbiased sequential recommendation with latent confounders. In: Proceedings of the ACM Web Conference 2022. p. 2195-2204. WWW \u201922, Association for Computing Machinery, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3485447.3512092, https:\/\/doi.org\/10.1145\/3485447.3512092","DOI":"10.1145\/3485447.3512092"},{"key":"11_CR15","doi-asserted-by":"publisher","unstructured":"Wang, Z., Zhang, J., Xu, H., Chen, X., Zhang, Y., Zhao, W.X., Wen, J.R.: Counterfactual data-augmented sequential recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. p. 347-356. SIGIR \u201921, Association for Computing Machinery, New York, NY, USA (2021). https:\/\/doi.org\/10.1145\/3404835.3462855, https:\/\/doi.org\/10.1145\/3404835.3462855","DOI":"10.1145\/3404835.3462855"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Wu, Y., Xie, R., Zhu, Y., Ao, X., Chen, X., Zhang, X., Zhuang, F., Lin, L., He, Q.: Multi-view multi-behavior contrastive learning in recommendation. In: International Conference on Database Systems for Advanced Applications. pp. 166\u2013182. Springer (2022)","DOI":"10.1007\/978-3-031-00126-0_11"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Xia, L., Huang, C., Xu, Y., Dai, P., Lu, M., Bo, L.: Multi-behavior enhanced recommendation with cross-interaction collaborative relation modeling. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE). pp. 1931\u20131936. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00179"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Xia, L., Xu, Y., Huang, C., Dai, P., Bo, L.: Graph meta network for multi-behavior recommendation. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. pp. 757\u2013766 (2021)","DOI":"10.1145\/3404835.3462972"},{"key":"11_CR19","unstructured":"Xu, G., Duong, T., Li, Q., Liu, S., Wang, X.: Causality learning: A new perspective for interpretable machine learning. IEEE Intelligent Informatics Bulletin (2020)"},{"key":"11_CR20","unstructured":"Xu, S., Tan, J., Heinecke, S., Li, V.J., Zhang, Y.: Deconfounded causal collaborative filtering. ACM Transactions on Recommender Systems (2021)"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Xuan, H., Li, B.: Temporal-aware multi-behavior contrastive recommendation. In: International Conference on Database Systems for Advanced Applications. pp. 269\u2013285. Springer (2023)","DOI":"10.1007\/978-3-031-30672-3_18"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Xuan, H., Liu, Y., Li, B., Yin, H.: Knowledge enhancement for contrastive multi-behavior recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. pp. 195\u2013203 (2023)","DOI":"10.1145\/3539597.3570386"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Yang, H., Chen, H., Li, L., Philip, S.Y., Xu, G.: Hyper meta-path contrastive learning for multi-behavior recommendation. In: 2021 IEEE International Conference on Data Mining (ICDM). pp. 787\u2013796. IEEE (2021)","DOI":"10.1109\/ICDM51629.2021.00090"},{"key":"11_CR24","doi-asserted-by":"publisher","unstructured":"Yang, H., Chen, H., Zhang, S., Sun, X., Li, Q., Zhao, X., Xu, G.: Generating counterfactual hard negative samples for graph contrastive learning. In: Proceedings of the ACM Web Conference 2023. p. 621-629. WWW \u201923, Association for Computing Machinery, New York, NY, USA (2023). https:\/\/doi.org\/10.1145\/3543507.3583499, https:\/\/doi.org\/10.1145\/3543507.3583499","DOI":"10.1145\/3543507.3583499"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.neucom.2023.01.089","volume":"529","author":"D Yu","year":"2023","unstructured":"Yu, D., Li, Q., Wang, X., Xu, G.: Deconfounded recommendation via causal intervention. Neurocomputing 529, 128\u2013139 (2023)","journal-title":"Neurocomputing"},{"key":"11_CR26","doi-asserted-by":"publisher","unstructured":"Yuan, E., Guo, W., He, Z., Guo, H., Liu, C., Tang, R.: Multi-behavior sequential transformer recommender. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. p. 1642-1652. SIGIR \u201922, Association for Computing Machinery, New York, NY, USA (2022). https:\/\/doi.org\/10.1145\/3477495.3532023, https:\/\/doi.org\/10.1145\/3477495.3532023","DOI":"10.1145\/3477495.3532023"},{"key":"11_CR27","doi-asserted-by":"publisher","unstructured":"Zhang, J., Zhang, Q., Ai, Z., Li, X.: Context-based user typicality collaborative filtering recommendation. Human-Centric Intelligent Systems 1, 43\u201353 (2021). https:\/\/doi.org\/10.2991\/hcis.k.210524.001, https:\/\/doi.org\/10.2991\/hcis.k.210524.001","DOI":"10.2991\/hcis.k.210524.001"},{"key":"11_CR28","doi-asserted-by":"publisher","unstructured":"Zhang, Q., Zhang, X., Liu, Y., Wang, H., Gao, M., Zhang, J., Guo, R.: Debiasing recommendation by learning identifiable latent confounders. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. p. 3353-3363. KDD \u201923, Association for Computing Machinery, New York, NY, USA (2023). https:\/\/doi.org\/10.1145\/3580305.3599296, https:\/\/doi.org\/10.1145\/3580305.3599296","DOI":"10.1145\/3580305.3599296"},{"key":"11_CR29","unstructured":"Zhu, X., Zhang, Y., Feng, F., Yang, X., Wang, D., He, X.: Mitigating hidden confounding effects for causal recommendation. arXiv preprint arXiv:2205.07499 (2022)"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5555-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,11]],"date-time":"2025-01-11T06:04:35Z","timestamp":1736575475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5555-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819755547","9789819755554"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5555-4_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"12 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gifu","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2024a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}