{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:45:11Z","timestamp":1780609511917,"version":"3.54.1"},"publisher-location":"Cham","reference-count":47,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031887161","type":"print"},{"value":"9783031887178","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\/978-3-031-88717-8_23","type":"book-chapter","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T12:08:27Z","timestamp":1743768507000},"page":"307-323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["How to\u00a0Diversify any Personalized Recommender?"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9048-1906","authenticated-orcid":false,"given":"Manel","family":"Slokom","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8888-2869","authenticated-orcid":false,"given":"Savvina","family":"Daniil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6865-0021","authenticated-orcid":false,"given":"Laura","family":"Hollink","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,3]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"An, M., Wu, F., Wu, C., Zhang, K., Liu, Z., Xie, X.: Neural news recommendation with long- and short-term user representations. In: Korhonen, A., Traum, D., M\u00e0rquez, L. (eds.) Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 336\u2013345 (2019)","DOI":"10.18653\/v1\/P19-1033"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Bellogin, A., Castells, P., Cantador, I.: Precision-oriented evaluation of recommender systems: an algorithmic comparison. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 333\u2013336 (2011)","DOI":"10.1145\/2043932.2043996"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Boratto, L., Fabbri, F., Fenu, G., Marras, M., Medda, G.: Fair augmentation for graph collaborative filtering. In: Proceedings of the 18th ACM Conference on Recommender Systems, pp. 158\u2013168 (2024)","DOI":"10.1145\/3640457.3688064"},{"issue":"3","key":"23_CR4","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/s11257-021-09294-8","volume":"31","author":"L Boratto","year":"2021","unstructured":"Boratto, L., Fenu, G., Marras, M.: Interplay between upsampling and regularization for provider fairness in recommender systems. User Model. User-Adap. Inter. 31(3), 421\u2013455 (2021). https:\/\/doi.org\/10.1007\/s11257-021-09294-8","journal-title":"User Model. User-Adap. Inter."},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Daniel, F., Kartik, H.: Blockbuster culture\u2019s next rise or fall: the impact of recommender systems on sales diversity. Manag. Sci. 55(5) (2009)","DOI":"10.1287\/mnsc.1080.0974"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Daniil, S., Cuper, M., Liem, C.C.S., van Ossenbruggen, J., Hollink, L.: Reproducing popularity bias in recommendation: the effect of evaluation strategies. ACM Trans. Recommend. Syst. (2023)","DOI":"10.1145\/3637066"},{"issue":"5","key":"23_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102662","volume":"58","author":"Y Deldjoo","year":"2021","unstructured":"Deldjoo, Y., Bellogin, A., Di Noia, T.: Explaining recommender systems fairness and accuracy through the lens of data characteristics. Inf. Process. Manag. 58(5), 102662 (2021)","journal-title":"Inf. Process. Manag."},{"issue":"1","key":"23_CR8","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1145\/963770.963776","volume":"22","author":"M Deshpande","year":"2004","unstructured":"Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143\u2013177 (2004)","journal-title":"ACM Trans. Inf. Syst."},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Ekstrand, M.D., Das, A., Burke, R., Diaz, F.: Fairness in Recommender Systems, pp. 679\u2013707. Springer, Boston (2022)","DOI":"10.1007\/978-1-0716-2197-4_18"},{"key":"23_CR10","unstructured":"Ekstrand, M.D., Joshaghani, R., Mehrpouyan, H.: Privacy for all: ensuring fair and equitable privacy protections. In: Friedler, S.A., Wilson, C. (eds.) Proceedings of the International Conference on Fairness, Accountability and Transparency. Proceedings of Machine Learning Research, vol.\u00a081, pp. 35\u201347 (2018)"},{"key":"23_CR11","unstructured":"Ekstrand, M.D., Tian, M., Azpiazu, I.M., Ekstrand, J.D., Anuyah, O., McNeill, D., Pera, M.S.: All the cool kids, how do they fit in?: popularity and demographic biases in recommender evaluation and effectiveness. In: Friedler, S.A., Wilson, C. (eds.) Proceedings of the International Conference on Fairness, Accountability and Transparency. Proceedings of Machine Learning Research, vol.\u00a081, pp. 172\u2013186. PMLR (2018)"},{"key":"23_CR12","doi-asserted-by":"publisher","unstructured":"Grisse, K.: Recommender Systems, Manipulation and Private Autonomy: How European Civil Law Regulates and Should Regulate Recommender Systems for the Benefit of Private Autonomy, pp. 101\u2013128. Springer, Heidelberg (2023). https:\/\/doi.org\/10.1007\/978-3-031-34804-4_6","DOI":"10.1007\/978-3-031-34804-4_6"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Hansen, C., Mehrotra, R., Hansen, C., Brost, B., Maystre, L., Lalmas, M.: Shifting consumption towards diverse content on music streaming platforms. In: Proceedings of the ACM International Conference on Web Search and Data Mining, pp. 238\u2013246 (2021)","DOI":"10.1145\/3437963.3441775"},{"issue":"2","key":"23_CR14","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1080\/1369118X.2016.1271900","volume":"21","author":"N Helberger","year":"2018","unstructured":"Helberger, N., Karppinen, K., D\u2019Acunto, L.: Exposure diversity as a design principle for recommender systems. Inf. Commun. Soc. 21(2), 191\u2013207 (2018)","journal-title":"Inf. Commun. Soc."},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: IEEE International Conference on Data Mining, pp. 263\u2013272 (2008)","DOI":"10.1109\/ICDM.2008.22"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7(1) (2016)","DOI":"10.1145\/2926720"},{"issue":"3","key":"23_CR17","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1023\/A:1008334909089","volume":"11","author":"K Lakshminarayan","year":"1999","unstructured":"Lakshminarayan, K., Harp, S.A., Samad, T.: Imputation of missing data in industrial databases. Appl. Intell. 11(3), 259\u2013275 (1999)","journal-title":"Appl. Intell."},{"key":"23_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.104960","volume":"186","author":"Q Liu","year":"2019","unstructured":"Liu, Q., Reiner, A.H., Frigessi, A., Scheel, I.: Diverse personalized recommendations with uncertainty from implicit preference data with the bayesian mallows model. Knowl.-Based Syst. 186, 104960 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"23_CR19","doi-asserted-by":"publisher","unstructured":"Liu, Y., Li, M., Ariannezhad, M., Mansoury, M., Aliannejadi, M., de\u00a0Rijke, M.: Measuring item fairness in next basket recommendation: a reproducibility study. In: Advances in Information Retrieval: 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, 24\u201328 March 2024, Proceedings, Part IV, pp. 210\u2013225. Springer, Heidelberg (2024). https:\/\/doi.org\/10.1007\/978-3-031-56066-8_18","DOI":"10.1007\/978-3-031-56066-8_18"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Mansoury, M., Abdollahpouri, H., Pechenizkiy, M., Mobasher, B., Burke, R.: Fairmatch: a graph-based approach for improving aggregate diversity in recommender systems. In: Proceedings of the ACM International Conference on User Modeling, Adaptation and Personalization, pp. 154\u2013162 (2020)","DOI":"10.1145\/3340631.3394860"},{"issue":"4","key":"23_CR21","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/s41019-023-00228-5","volume":"8","author":"X Meng","year":"2023","unstructured":"Meng, X., Huo, H., Zhang, X., Wang, W., Zhu, J.: A survey of personalized news recommendation. Data Sci. Eng. 8(4), 396\u2013416 (2023)","journal-title":"Data Sci. Eng."},{"key":"23_CR22","doi-asserted-by":"crossref","unstructured":"Michiels, L., Vannieuwenhuyze, J., Leysen, J., Verachtert, R., Smets, A., Goethals, B.: How should we measure filter bubbles? a regression model and evidence for online news. In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 640\u2013651 (2023)","DOI":"10.1145\/3604915.3608805"},{"key":"23_CR23","unstructured":"Oliveira, R.S., N\u00f3brega, C., Marinho, L.B., Andrade, N.: A multiobjective music recommendation approach for aspect-based diversification. In: Proceedings of the International Society for Music Information Retrieval Conference, pp. 414\u2013420 (2017)"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Pariser, E.: The filter bubble: what the internet is hiding from you. Penguin UK (2011)","DOI":"10.3139\/9783446431164"},{"issue":"3","key":"23_CR25","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1007\/s00778-021-00697-y","volume":"31","author":"E Pitoura","year":"2021","unstructured":"Pitoura, E., Stefanidis, K., Koutrika, G.: Fairness in rankings and recommendations: an overview. VLDB J. 31(3), 431\u2013458 (2021)","journal-title":"VLDB J."},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Rastegarpanah, B., Gummadi, K.P., Crovella, M.: Fighting fire with fire: using antidote data to improve polarization and fairness of recommender systems. In: Proceedings of the 12th ACM International Conference on Web Search and Data Mining, pp. 231\u2013239 (2019)","DOI":"10.1145\/3289600.3291002"},{"key":"23_CR27","unstructured":"Raza, S., Bashir, S.R., Naseem, U.: Accuracy meets diversity in a news recommender system. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 3778\u20133787 (2022)"},{"key":"23_CR28","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: bayesian personalized ranking from implicit feedback. In: Proceedings of the International Conference on Uncertainty in Artificial Intelligence, pp. 452\u2013461. AUAI Press (2009)"},{"key":"23_CR29","unstructured":"Rubinsteyn, A., Feldman, S.: fancyimpute: an imputation library for python. https:\/\/github.com\/iskandr\/fancyimpute"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Slokom, M., Hanjalic, A., Larson, M.: Towards user-oriented privacy for recommender system data: a personalization-based approach to gender obfuscation for user profiles. Inf. Process. Manag. 58(6) (2021)","DOI":"10.1016\/j.ipm.2021.102722"},{"key":"23_CR31","doi-asserted-by":"publisher","unstructured":"Smyth, B., McClave, P.: Similarity vs. diversity. In: Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development, pp. 347\u2013361. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44593-5_25","DOI":"10.1007\/3-540-44593-5_25"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Stamenkovic, D., Karatzoglou, A., Arapakis, I., Xin, X., Katevas, K.: Choosing the best of both worlds: diverse and novel recommendations through multi-objective reinforcement learning. In: Proceedings of the 15th ACM International Conference on Web Search and Data Mining, pp. 957\u2013965 (2022)","DOI":"10.1145\/3488560.3498471"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Su, X., Khoshgoftaar, T.M., Greiner, R.: Imputed neighborhood based collaborative filtering. In: International Conference on Web Intelligence and Intelligent Agent Technology, vol.\u00a01, pp. 633\u2013639. IEEE (2008)","DOI":"10.1109\/WIIAT.2008.99"},{"key":"23_CR34","doi-asserted-by":"crossref","unstructured":"Treuillier, C., Castagnos, S., Dufraisse, E., Brun, A.: Being diverse is not enough: rethinking diversity evaluation to meet challenges of news recommender systems. In: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization, pp. 222\u2013233 (2022)","DOI":"10.1145\/3511047.3538030"},{"key":"23_CR35","doi-asserted-by":"crossref","unstructured":"Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the ACM International Conference on Recommender Systems, pp. 109\u2013116 (2011)","DOI":"10.1145\/2043932.2043955"},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Vrijenhoek, S., B\u00e9n\u00e9dict, G., Gutierrez\u00a0Granada, M., Odijk, D., De\u00a0Rijke, M.: Radio \u2013 rank-aware divergence metrics to measure normative diversity in news recommendations. In: Proceedings of the ACM International Conference on Recommender Systems, pp. 208\u2013219 (2022)","DOI":"10.1145\/3523227.3546780"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Weinsberg, U., Bhagat, S., Ioannidis, S., Taft, N.: BlurMe: inferring and obfuscating user gender based on ratings. In: Proceedings of the ACM International Conference on Recommender Systems, pp. 195\u2013202 (2012)","DOI":"10.1145\/2365952.2365989"},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., An, M., Huang, J., Huang, Y., Xie, X.: Npa: neural news recommendation with personalized attention. In: Proceedings of the ACM International Conference on Knowledge Discovery & Data Mining, p. 2576\u20132584 (2019)","DOI":"10.1145\/3292500.3330665"},{"key":"23_CR39","doi-asserted-by":"crossref","unstructured":"Wu, C., Wu, F., Ge, S., Qi, T., Huang, Y., Xie, X.: Neural news recommendation with multi-head self-attention. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) Proceedings of the ACM International Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6389\u20136394 (2019)","DOI":"10.18653\/v1\/D19-1671"},{"key":"23_CR40","unstructured":"Wu, C., Wu, F., Qi, T., Huang, Y.: End-to-end learnable diversity-aware news recommendation. arXiv preprint arXiv:2204.00539 (2022)"},{"key":"23_CR41","doi-asserted-by":"crossref","unstructured":"Wu, F., et al.: MIND: a large-scale dataset for news recommendation. In: Jurafsky, D., Chai, J., Schluter, N., Tetreault, J. (eds.) Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 3597\u20133606. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.331"},{"key":"23_CR42","doi-asserted-by":"crossref","unstructured":"Wu, Y., Cao, J., Xu, G.: Fairness in recommender systems: evaluation approaches and assurance strategies. ACM Trans. Knowl. Disc. Data 18(1) (2023)","DOI":"10.1145\/3604558"},{"key":"23_CR43","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 ACM on Conference on Information and Knowledge Management, pp. 1569\u20131578 (2017)","DOI":"10.1145\/3132847.3132938"},{"key":"23_CR44","doi-asserted-by":"crossref","unstructured":"Zehlike, M., S\u00fchr, T., Baeza-Yates, R., Bonchi, F., Castillo, C., Hajian, S.: Fair top-k ranking with multiple protected groups. Inf. Process. Manag. 59(1) (2022)","DOI":"10.1016\/j.ipm.2021.102707"},{"key":"23_CR45","unstructured":"Zhao, Y., Wang, Y., Liu, Y., Cheng, X., Aggarwal, C., Derr, T.: Fairness and diversity in recommender systems: a survey. arXiv preprint arXiv:2307.04644 (2023)"},{"key":"23_CR46","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Gao, C., Chen, L., Jin, D., Li, Y.: DGCN: diversified recommendation with graph convolutional networks. In: Proceedings of the Web Conference 2021, pp. 401\u2013412. Association for Computing Machinery (2021)","DOI":"10.1145\/3442381.3449835"},{"key":"23_CR47","doi-asserted-by":"crossref","unstructured":"Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22\u201332 (2005)","DOI":"10.1145\/1060745.1060754"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88717-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T09:24:08Z","timestamp":1746696248000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88717-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031887161","9783031887178"],"references-count":47,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88717-8_23","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":"3 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lucca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"47","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2025.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}