{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:24:15Z","timestamp":1743035055409,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466762"},{"type":"electronic","value":"9783031466779"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-46677-9_26","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"374-388","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Calibrating Popularity Bias Based on\u00a0Quality for\u00a0Recommendation Fairness"],"prefix":"10.1007","author":[{"given":"Zhengyi","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanmin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaobo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyuan","family":"Jing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Abdollahpouri, H., Burke, R., Mobasher, B.: Controlling popularity bias in learning-to-rank recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 42\u201346. RecSys \u201917, New York, NY, USA (2017)","DOI":"10.1145\/3109859.3109912"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Beutel, A., et al.: Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2212\u20132220. KDD \u201919, New York, NY, USA (2019)","DOI":"10.1145\/3292500.3330745"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Bonner, S., Vasile, F.: Causal embeddings for recommendation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 104\u2013112. RecSys \u201918, New York, NY, USA (2018)","DOI":"10.1145\/3240323.3240360"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Celma, O., Cano, P.: From hits to niches? or how popular artists can bias music recommendation and discovery. In: Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition. NETFLIX \u201908, New York, NY, USA (2008)","DOI":"10.1145\/1722149.1722154"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Chen, C., Zhang, M., Liu, Y., Ma, S.: Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 World Wide Web Conference, pp. 1583\u20131592. WWW \u201918, Republic and Canton of Geneva, CHE (2018)","DOI":"10.1145\/3178876.3186070"},{"issue":"5","key":"26_CR6","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1007\/s11257-015-9165-3","volume":"25","author":"D Jannach","year":"2015","unstructured":"Jannach, D., Lerche, L., Kamehkhosh, I., Jugovac, M.: What recommenders recommend: an analysis of recommendation biases and possible countermeasures. User Model. User-Adap. Inter. 25(5), 427\u2013491 (2015)","journal-title":"User Model. User-Adap. Inter."},{"key":"26_CR7","unstructured":"Liang, D., Charlin, L., Blei, D.M.: Causal inference for recommendation. In: Causation: Foundation to Application, Workshop at UAI. AUAI (2016)"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Liu, D., Li, J., Du, B., Chang, J., Gao, R.: Daml: dual attention mutual learning between ratings and reviews for item recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 344\u2013352. KDD \u201919, New York, NY, USA (2019)","DOI":"10.1145\/3292500.3330906"},{"key":"26_CR9","unstructured":"Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems 20 (2007)"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Park, Y.J., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 11\u201318. RecSys \u201908, New York, NY, USA (2008)","DOI":"10.1145\/1454008.1454012"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Ren, W., Wang, L., Liu, K., Guo, R., Lim, E., Fu, Y.: Mitigating popularity bias in recommendation with unbalanced interactions: A gradient perspective. In: IEEE International Conference on Data Mining, ICDM 2022, Orlando, FL, USA, November 28 - Dec. 1, 2022., pp. 438\u2013447. IEEE (2022)","DOI":"10.1109\/ICDM54844.2022.00054"},{"key":"26_CR12","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. CoRR abs\/1205.2618 (2012)"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2219\u20132228. KDD \u201918, New York, NY, USA (2018)","DOI":"10.1145\/3219819.3220088"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Steck, H.: Item popularity and recommendation accuracy. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 125\u2013132. RecSys \u201911, New York, NY, USA (2011)","DOI":"10.1145\/2043932.2043957"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Wei, T., Feng, F., Chen, J., Wu, Z., Yi, J., He, X.: Model-Agnostic Counterfactual Reasoning for Eliminating Popularity Bias in Recommender System, pp. 1791\u20131800. New York, NY, USA (2021)","DOI":"10.1145\/3447548.3467289"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Xv, G., Lin, C., Li, H., Su, J., Ye, W., Chen, Y.: Neutralizing popularity bias in recommendation models. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2623\u20132628. SIGIR \u201922 (2022)","DOI":"10.1145\/3477495.3531907"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., Baeza-Yates, R.: Fa*ir: A fair top-k ranking algorithm. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1569\u20131578. CIKM \u201917, New York, NY, USA (2017)","DOI":"10.1145\/3132847.3132938"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Causal intervention for leveraging popularity bias in recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 11\u201320 (2021)","DOI":"10.1145\/3404835.3462875"},{"key":"26_CR19","unstructured":"Zhao, Z., et al.: Popularity bias is not always evil: disentangling benign and harmful bias for recommendation. IEEE Trans. Knowl. Data Eng. 1\u201313 (2022)"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 425\u2013434. WSDM \u201917, New York, NY, USA (2017)","DOI":"10.1145\/3018661.3018665"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Gao, C., Li, X., He, X., Li, Y., Jin, D.: Disentangling User Interest and Conformity for Recommendation with Causal Embedding, p. 2980\u20132991. New York, NY, USA (2021)","DOI":"10.1145\/3442381.3449788"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, Z., He, Y., Zhao, X., Zhang, Y., Wang, J., Caverlee, J.: Popularity-opportunity bias in collaborative filtering. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 85\u201393. WSDM \u201921, New York, NY, USA (2021)","DOI":"10.1145\/3437963.3441820"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Hu, X., Caverlee, J.: Fairness-aware tensor-based recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1153\u20131162. CIKM \u201918, New York, NY, USA (2018)","DOI":"10.1145\/3269206.3271795"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46677-9_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:24:36Z","timestamp":1699104276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46677-9_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466762","9783031466779"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46677-9_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.97","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.77","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}