{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T12:40:01Z","timestamp":1751287201868,"version":"3.41.0"},"reference-count":90,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T00:00:00Z","timestamp":1742860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004385","name":"Universiteit Gent","doi-asserted-by":"publisher","award":["BOFGOA2018000601"],"award-info":[{"award-number":["BOFGOA2018000601"]}],"id":[{"id":"10.13039\/501100004385","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["User Model User-Adap Inter"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11257-025-09429-1","type":"journal-article","created":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:56:48Z","timestamp":1743119808000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving consumption diversity via graph-based topic nudging"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2035-256X","authenticated-orcid":false,"given":"Stefaan","family":"Vercoutere","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Glen","family":"Joris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toon","family":"De Pessemier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc","family":"Martens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,25]]},"reference":[{"key":"9429_CR1","doi-asserted-by":"publisher","unstructured":"Abdollahpouri, H.: Popularity bias in ranking and recommendation. In: Proceedings of the 2019 AAAI\/ACM Conference on AI, Ethics, and Society. AIES \u201919, pp. 529\u2013530. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3306618.3314309","DOI":"10.1145\/3306618.3314309"},{"issue":"5","key":"9429_CR2","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1109\/TKDE.2011.15","volume":"24","author":"G Adomavicius","year":"2012","unstructured":"Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896\u2013911 (2012). https:\/\/doi.org\/10.1109\/TKDE.2011.15","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"9429_CR3","doi-asserted-by":"publisher","unstructured":"Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: Proceedings of the Second ACM International Conference on Web Search and Data Mining. WSDM \u201909, pp. 5\u201314. Association for Computing Machinery, New York, NY, USA (2009). https:\/\/doi.org\/10.1145\/1498759.1498766","DOI":"10.1145\/1498759.1498766"},{"key":"9429_CR4","unstructured":"Baker, J.E.: Reducing bias and inefficiency in the selection algorithm. In: Proceedings of the Second International Conference on Genetic Algorithms on Genetic Algorithms and Their Application, pp. 14\u201321. L. Erlbaum Associates Inc., USA (1987)"},{"key":"9429_CR5","doi-asserted-by":"publisher","unstructured":"Caraban, A., Karapanos, E., Gon\u00e7alves, D., Campos, P.: 23 ways to nudge: A review of technology-mediated nudging in human-computer interaction. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. CHI \u201919, pp. 1\u201315. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3290605.3300733","DOI":"10.1145\/3290605.3300733"},{"key":"9429_CR6","doi-asserted-by":"publisher","unstructured":"Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201998, pp. 335\u2013336. Association for Computing Machinery, New York, NY, USA (1998). https:\/\/doi.org\/10.1145\/290941.291025","DOI":"10.1145\/290941.291025"},{"key":"9429_CR7","doi-asserted-by":"publisher","unstructured":"Carterette, B., Chandar, P.: Probabilistic models of ranking novel documents for faceted topic retrieval. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. CIKM \u201909, pp. 1287\u20131296. Association for Computing Machinery, New York, NY, USA (2009). https:\/\/doi.org\/10.1145\/1645953.1646116","DOI":"10.1145\/1645953.1646116"},{"key":"9429_CR8","unstructured":"Castagnos, S., Brun, A., Boyer, A.: When diversity is needed... but not expected! In: International Conference on Advances in Information Mining and Management, pp. 44\u201350. IARIA XPS Press, Lisbon, Portugal (2013). https:\/\/inria.hal.science\/hal-00931805"},{"key":"9429_CR9","unstructured":"Castells, P., Vargas, S., Wang, J.: Novelty and diversity metrics for recommender systems: Choice, discovery and relevance. In: Proceedings of International Workshop on Diversity in Document Retrieval (DDR). Association for Computing Machinery, New York, NY, USA (2011)"},{"key":"9429_CR10","doi-asserted-by":"publisher","unstructured":"Clarke, C., Kolla, M., Cormack, G., Vechtomova, O., Ashkan, A., B\u00fcttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st ACM SIGIR, pp. 659\u2013666. Association for Computing Machinery, New York, NY, USA (2008). https:\/\/doi.org\/10.1145\/1390334.1390446","DOI":"10.1145\/1390334.1390446"},{"key":"9429_CR11","doi-asserted-by":"publisher","unstructured":"Darvishy, A., Ibrahim, H., Sidi, F., Mustapha, A.: Hypner: A hybrid approach for personalized news recommendation. IEEE Access 8, 46877\u201346894 (2020) https:\/\/doi.org\/10.1109\/ACCESS.2020.2978505","DOI":"10.1109\/ACCESS.2020.2978505"},{"key":"9429_CR12","doi-asserted-by":"publisher","unstructured":"Das, A., Datar, M., Garg, A., Rajaram, S.: Google news personalization: Scalable online collaborative filtering. In: Proceedings of the 16th International Conference on World Wide Web, pp. 271\u2013280. Association for Computing Machinery, New York, NY, USA (2007). https:\/\/doi.org\/10.1145\/1242572.1242610","DOI":"10.1145\/1242572.1242610"},{"key":"9429_CR13","first-page":"37","volume":"10","author":"O De Clercq","year":"2020","unstructured":"De Clercq, O., De Bruyne, L., Hoste, V.: News topic classification as a first step towards diverse news recommendation. Computational Linguistics in the Netherlands Journal 10, 37\u201355 (2020)","journal-title":"Computational Linguistics in the Netherlands Journal"},{"key":"9429_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2016.11.015","author":"T Di Noia","year":"2016","unstructured":"Di Noia, T., Rosati, J., Tomeo, P., Di Sciascio, E.: Adaptive multi-attribute diversity for recommender systems. Inf. Sci. (2016). https:\/\/doi.org\/10.1016\/j.ins.2016.11.015","journal-title":"Inf. Sci."},{"key":"9429_CR15","doi-asserted-by":"publisher","unstructured":"Di\u00a0Noia, T., Ostuni, V., Rosati, J., Tomeo, P., Di\u00a0Sciascio, E.: An analysis of users\u2019 propensity toward diversity in recommendations. In: Proceedings of the 8th ACM Conference on Recommender Systems. Association for Computing Machinery, New York, NY, USA (2014). https:\/\/doi.org\/10.1145\/2645710.2645774","DOI":"10.1145\/2645710.2645774"},{"key":"9429_CR16","doi-asserted-by":"publisher","unstructured":"Dijkstra, E.W.: A note on two problems in connexion with graphs, 1st edn., pp. 287\u2013290. Association for Computing Machinery, New York, NY, USA (1959). https:\/\/doi.org\/10.1145\/3544585.3544600","DOI":"10.1145\/3544585.3544600"},{"key":"9429_CR17","doi-asserted-by":"publisher","unstructured":"Elsweiler, D., Trattner, C., Harvey, M.: Exploiting food choice biases for healthier recipe recommendation. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR \u201917, pp. 575\u2013584. Association for Computing Machinery, New York, NY, USA (2017). https:\/\/doi.org\/10.1145\/3077136.3080826","DOI":"10.1145\/3077136.3080826"},{"key":"9429_CR18","doi-asserted-by":"publisher","unstructured":"Eskandanian, F., Mobasher, B., Burke, R.: A clustering approach for personalizing diversity in collaborative recommender systems. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 280\u2013284. Association for Computing Machinery, New York, NY, USA (2017). https:\/\/doi.org\/10.1145\/3079628.3079699","DOI":"10.1145\/3079628.3079699"},{"issue":"6","key":"9429_CR19","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1145\/367766.368168","volume":"5","author":"RW Floyd","year":"1962","unstructured":"Floyd, R.W.: Algorithm 97: Shortest path. Commun. ACM 5(6), 345 (1962). https:\/\/doi.org\/10.1145\/367766.368168","journal-title":"Commun. ACM"},{"issue":"2","key":"9429_CR20","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1093\/biomet\/85.2.379","volume":"85","author":"DP Foster","year":"1998","unstructured":"Foster, D.P., Vohra, R.V.: Asymptotic calibration. Biometrika 85(2), 379\u2013390 (1998)","journal-title":"Biometrika"},{"key":"9429_CR21","doi-asserted-by":"publisher","unstructured":"Frey, D.: Recent Research on Selective Exposure to Information, vol. 19, pp. 41\u201380. Elsevier, ??? (1986). https:\/\/doi.org\/10.1016\/S0065-2601(08)60212-9","DOI":"10.1016\/S0065-2601(08)60212-9"},{"key":"9429_CR22","doi-asserted-by":"publisher","unstructured":"Gemmis, M., Lops, P., Semeraro, G., Musto, C.: An investigation on the serendipity problem in recommender systems. Information Processing and Management 51, 695\u2013717 (2015) https:\/\/doi.org\/10.1016\/j.ipm.2015.06.008","DOI":"10.1016\/j.ipm.2015.06.008"},{"key":"9429_CR23","doi-asserted-by":"publisher","unstructured":"Gena, C., Grillo, P., Lieto, A., Mattutino, C., Vernero, F.: When personalization is not an option: An in-the-wild study on persuasive news recommendation. Information 10(10) (2019) https:\/\/doi.org\/10.3390\/info10100300","DOI":"10.3390\/info10100300"},{"key":"9429_CR24","unstructured":"Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. International Conference on Learning Representations 2016 (2015)"},{"key":"9429_CR25","doi-asserted-by":"publisher","unstructured":"Holanda\u00a0Coelho, G., HP\u00a0Hanel, P., J.\u00a0Wolf, L.: The very efficient assessment of need for cognition: Developing a six-item version. Assessment 27(8), 1870\u20131885 (2020) https:\/\/doi.org\/10.1177\/1073191118793208","DOI":"10.1177\/1073191118793208"},{"key":"9429_CR26","doi-asserted-by":"publisher","unstructured":"Huang, P.-S., He, X., Gao, J., Deng, l., Acero, A., Heck, L.: Learning deep structured semantic models for web search using clickthrough data. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2333\u20132338. Association for Computing Machinery, New York, NY, USA (2013). https:\/\/doi.org\/10.1145\/2505515.2505665","DOI":"10.1145\/2505515.2505665"},{"key":"9429_CR27","doi-asserted-by":"publisher","unstructured":"IJntema, W., Goossen, F., Frasincar, F., Hogenboom, F.: Ontology-based news recommendation. In: Proceedings of the 2010 EDBT\/ICDT Workshops. Association for Computing Machinery, New York, NY, USA (2010). https:\/\/doi.org\/10.1145\/1754239.1754257","DOI":"10.1145\/1754239.1754257"},{"key":"9429_CR28","doi-asserted-by":"publisher","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems 20, 422\u2013446 (2002) https:\/\/doi.org\/10.1145\/582415.582418","DOI":"10.1145\/582415.582418"},{"key":"9429_CR29","doi-asserted-by":"publisher","unstructured":"Jesse, M., Jannach, D.: Digital nudging with recommender systems: Survey and future directions. Computers in Human Behavior Reports 3, 100052 (2021) https:\/\/doi.org\/10.1016\/j.chbr.2020.100052","DOI":"10.1016\/j.chbr.2020.100052"},{"key":"9429_CR30","unstructured":"Jiang, J.J., Conrath, D.W.: Semantic similarity based on corpus statistics and lexical taxonomy. In: Proceedings of the 10th Research on Computational Linguistics International Conference, pp. 19\u201333. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP), Taipei, Taiwan (1997). https:\/\/aclanthology.org\/O97-1002"},{"key":"9429_CR31","doi-asserted-by":"publisher","unstructured":"Jones, R., Carter, R.: Some procedures for estimating \"news hole\" in content analysis. Public Opinion Quarterly 23, 399\u2013403 (1959)https:\/\/doi.org\/10.1086\/266892","DOI":"10.1086\/266892"},{"key":"9429_CR32","doi-asserted-by":"publisher","unstructured":"Joris, G., De\u00a0Grove, F., Van\u00a0Damme, K., Marez, L.: Appreciating news algorithms: Examining audiences\u2019 perceptions to different news selection mechanisms. Digital Journalism 9, 1\u201330 (2021) https:\/\/doi.org\/10.1080\/21670811.2021.1912626","DOI":"10.1080\/21670811.2021.1912626"},{"key":"9429_CR33","doi-asserted-by":"publisher","unstructured":"Joseph, K., Jiang, H.: Content based news recommendation via shortest entity distance over knowledge graphs. In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 690\u2013699. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3308560.3317703","DOI":"10.1145\/3308560.3317703"},{"key":"9429_CR34","doi-asserted-by":"publisher","unstructured":"Jugovac, M., Jannach, D., Lerche, L.: Efficient optimization of multiple recommendation quality factors according to individual user tendencies. Expert Systems with Applications 81, 321\u2013331 (2017) https:\/\/doi.org\/10.1016\/j.eswa.2017.03.055","DOI":"10.1016\/j.eswa.2017.03.055"},{"key":"9429_CR35","doi-asserted-by":"crossref","unstructured":"Kapoor, K., Kumar, V., Terveen, L., Konstan, J.A., Schrater, P.:\u201ci like to explore sometimes\u201d adapting to dynamic user novelty preferences. In: Proceedings of the 9th ACM Conference on Recommender Systems, pp. 19\u201326. Association for Computing Machinery, New York, NY, USA (2015)","DOI":"10.1145\/2792838.2800172"},{"key":"9429_CR36","doi-asserted-by":"publisher","unstructured":"Kastenm\u00fcller, A., Greitemeyer, T., Jonas, E., Fischer, P., Frey, D.: Selective exposure: The impact of collectivism and individualism. The British journal of social psychology \/ the British Psychological Society 49, 745\u201363 (2010) https:\/\/doi.org\/10.1348\/014466609X478988","DOI":"10.1348\/014466609X478988"},{"key":"9429_CR37","doi-asserted-by":"publisher","unstructured":"Kunaver, M., Po\u017erl, T.: Diversity in recommender systems - a survey. Knowledge-Based Systems 123, 154\u2013162 (2017) https:\/\/doi.org\/10.1016\/j.knosys.2017.02.009","DOI":"10.1016\/j.knosys.2017.02.009"},{"key":"9429_CR38","doi-asserted-by":"publisher","unstructured":"Lazarsfeld, P.F., Berelson, B., Gaudet, H.: The People\u2019s Choice: How the Voter Makes up His Mind in a Presidential Campaign. Columbia University Press, New York Chichester, West Sussex (1968). https:\/\/doi.org\/10.7312\/laza93930","DOI":"10.7312\/laza93930"},{"key":"9429_CR39","doi-asserted-by":"publisher","unstructured":"LEE, J., Kim, M.H., Lee, Y.: Information retrieval based on conceptual distance in a is-a hierarchy. j doc 49: 188-207. Journal of Documentation - J DOC 49, 188\u2013207 (1993) https:\/\/doi.org\/10.1108\/eb026913","DOI":"10.1108\/eb026913"},{"key":"9429_CR40","unstructured":"Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188\u20131196 (2014). PMLR"},{"key":"9429_CR41","doi-asserted-by":"crossref","unstructured":"Lenhart, P., Herzog, D.: Combining content-based and collaborative filtering for personalized sports news recommendations. In: CBRecSys@ RecSys, pp. 3\u201310. Association for Computing Machinery, New York, NY, USA (2016)","DOI":"10.5220\/0005763702930303"},{"key":"9429_CR42","doi-asserted-by":"publisher","unstructured":"Lenka, D., Barik, D.R., Panigrahi, S., Panda, S.: An improved hybrid distributed collaborative filtering model for recommender engine using apache spark. International Journal of Intelligent Systems and Applications 10, 74\u201381 (2018) https:\/\/doi.org\/10.5815\/ijisa.2018.07.08","DOI":"10.5815\/ijisa.2018.07.08"},{"key":"9429_CR43","doi-asserted-by":"publisher","unstructured":"Li, L., Wang, D., Li, T., Knox, D., Padmanabhan, B.: Scene: a scalable two-stage personalized news recommendation system. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 125\u2013134. Association for Computing Machinery, New York, NY, USA (2011). https:\/\/doi.org\/10.1145\/2009916.2009937","DOI":"10.1145\/2009916.2009937"},{"key":"9429_CR44","doi-asserted-by":"publisher","unstructured":"Liang, H., Xu, Y., Tjondronegoro, D., Christen, P.: Time-aware topic recommendation based on micro-blogs. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 1657\u20131661. Association for Computing Machinery, New York, NY, USA (2012). https:\/\/doi.org\/10.1145\/2396761.2398492","DOI":"10.1145\/2396761.2398492"},{"key":"9429_CR45","doi-asserted-by":"publisher","unstructured":"Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, pp. 31\u201340. Association for Computing Machinery, New York, NY, USA (2010). https:\/\/doi.org\/10.1145\/1719970.1719976","DOI":"10.1145\/1719970.1719976"},{"issue":"3\u20134","key":"9429_CR46","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1080\/13614568.2015.1036136","volume":"21","author":"H Liu","year":"2015","unstructured":"Liu, H., Bai, X., Yang, Z., Tolba, A., Xia, F.: Trust-aware recommendation for improving aggregate diversity. New Review of Hypermedia and Multimedia 21(3\u20134), 242\u2013258 (2015). https:\/\/doi.org\/10.1080\/13614568.2015.1036136","journal-title":"New Review of Hypermedia and Multimedia"},{"issue":"2","key":"9429_CR47","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1109\/TII.2014.2308433","volume":"10","author":"X Luo","year":"2014","unstructured":"Luo, X., Zhou, M., Xia, Y., Zhu, Q.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans. Industr. Inf. 10(2), 1273\u20131284 (2014)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"9429_CR48","unstructured":"Luostarinen, T., Kohonen, O.: Using topic models in content-based news recommender systems. In: Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013), pp. 239\u2013251. Link\u00f6ping University Electronic Press, Sweden, ??? (2013)"},{"key":"9429_CR49","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: Proceedings of the 30rd International Conference on International Conference on Machine Learning, vol. 30, p. 3 (2013)"},{"key":"9429_CR50","doi-asserted-by":"crossref","unstructured":"McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI\u201906 Extended Abstracts on Human Factors in Computing Systems, pp. 1097\u20131101. Association for Computing Machinery, New York, NY, USA (2006)","DOI":"10.1145\/1125451.1125659"},{"key":"9429_CR51","doi-asserted-by":"crossref","unstructured":"McQuail, D.: Media Performance: Mass Communication and the Public Interest vol. 144. Sage London, ??? (1992)","DOI":"10.22230\/cjc.1993v18n4a783"},{"issue":"3","key":"9429_CR52","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1177\/001654928303100301","volume":"31","author":"D McQuail","year":"1983","unstructured":"McQuail, D., Van Cuilenburg, J.J.: Diversity as a media policy goal: a strategy for evaluative research and a netherlands case study. Gazette (Leiden, Netherlands) 31(3), 145\u2013162 (1983). https:\/\/doi.org\/10.1177\/001654928303100301","journal-title":"Gazette (Leiden, Netherlands)"},{"key":"9429_CR53","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201913, pp. 3111\u20133119. Curran Associates Inc., Red Hook, NY, USA (2013)"},{"key":"9429_CR54","unstructured":"Mirsch, T., Lehrer, C., Jung, R.: Digital nudging: Altering user behavior in digital environments. Proceedings Der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017), 634\u2013648 (2017). Cited by: 89"},{"issue":"2","key":"9429_CR55","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1177\/0267323120966849","volume":"36","author":"H Moe","year":"2021","unstructured":"Moe, H., Hovden, J.F., Karppinen, K.: Operationalizing exposure diversity. Eur. J. Commun. 36(2), 148\u2013167 (2021). https:\/\/doi.org\/10.1177\/0267323120966849","journal-title":"Eur. J. Commun."},{"key":"9429_CR56","doi-asserted-by":"publisher","unstructured":"Moreira, G.: Chameleon: a deep learning meta-architecture for news recommender systems. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 578\u2013583. Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3240323.3240331","DOI":"10.1145\/3240323.3240331"},{"key":"9429_CR57","doi-asserted-by":"publisher","unstructured":"Musto, C., Semeraro, G., Gemmis, M., Lops, P.: Learning word embeddings from wikipedia for content-based recommender systems. In: European Conference on Information Retrieval, vol. 9626, pp. 729\u2013734. Springer, ??? (2016). https:\/\/doi.org\/10.1007\/978-3-319-30671-1_60","DOI":"10.1007\/978-3-319-30671-1_60"},{"issue":"6","key":"9429_CR58","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1007\/s10796-017-9782-y","volume":"20","author":"TT Nguyen","year":"2018","unstructured":"Nguyen, T.T., Maxwell Harper, F., Terveen, L., Konstan, J.A.: User personality and user satisfaction with recommender systems. Inf. Syst. Front. 20(6), 1173\u20131189 (2018). https:\/\/doi.org\/10.1007\/s10796-017-9782-y","journal-title":"Inf. Syst. Front."},{"key":"9429_CR59","doi-asserted-by":"publisher","unstructured":"Niemann, K., Wolpers, M.: A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 955\u2013963. Association for Computing Machinery, New York, NY, USA (2013). https:\/\/doi.org\/10.1145\/2487575.2487656","DOI":"10.1145\/2487575.2487656"},{"key":"9429_CR60","doi-asserted-by":"crossref","unstructured":"Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the sparsity problem of collaborative filtering using trust inferences. In: International Conference on Trust Management, pp. 224\u2013239 (2005). Springer","DOI":"10.1007\/11429760_16"},{"key":"9429_CR61","doi-asserted-by":"publisher","unstructured":"Pariser, E.: The Filter Bubble: What the Internet Is Hiding from You, p. 304. Penguin Books, ??? (2011). https:\/\/doi.org\/10.3139\/9783446431164","DOI":"10.3139\/9783446431164"},{"key":"9429_CR62","unstructured":"Paterek, A.: Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop, vol. 2007, pp. 5\u20138. Association for Computing Machinery, New York, NY, USA (2007)"},{"key":"9429_CR63","doi-asserted-by":"publisher","unstructured":"Phelan, O., McCarthy, K., Bennett, M., Smyth, B.: Terms of a feather: content-based news recommendation and discovery using twitter. In: European Conference on Information Retrieval, vol. 6611, pp. 448\u2013459. Springer, ??? (2011). https:\/\/doi.org\/10.1007\/978-3-642-20161-5_44 . Springer","DOI":"10.1007\/978-3-642-20161-5_44"},{"issue":"1","key":"9429_CR64","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/21.24528","volume":"19","author":"R Rada","year":"1989","unstructured":"Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. Syst. Man Cybern. 19(1), 17\u201330 (1989)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"9429_CR65","unstructured":"Rehurek, R., Sojka, P.: Gensim\u2013python framework for vector space modelling. NLP Centre, Faculty of Informatics, Masaryk University, Brno, Czech Republic 3(2) (2011)"},{"key":"9429_CR66","doi-asserted-by":"publisher","unstructured":"Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, pp. 175\u2013186. Association for Computing Machinery, New York, NY, USA (1994). https:\/\/doi.org\/10.1145\/192844.192905","DOI":"10.1145\/192844.192905"},{"key":"9429_CR67","unstructured":"Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 1. IJCAI\u201995, pp. 448\u2013453. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995)"},{"key":"9429_CR68","doi-asserted-by":"publisher","unstructured":"Santos, R., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, WWW \u201910, pp. 881\u2013890. Association for Computing Machinery, New York, NY, USA (2010). https:\/\/doi.org\/10.1145\/1772690.1772780","DOI":"10.1145\/1772690.1772780"},{"issue":"6","key":"9429_CR69","doi-asserted-by":"publisher","first-page":"1370","DOI":"10.1080\/09672567.2018.1523206","volume":"25","author":"E-M Sent","year":"2018","unstructured":"Sent, E.-M.: Rationality and bounded rationality: you can\u2019t have one without the other. The European Journal of the History of Economic Thought 25(6), 1370\u20131386 (2018). https:\/\/doi.org\/10.1080\/09672567.2018.1523206","journal-title":"The European Journal of the History of Economic Thought"},{"key":"9429_CR70","doi-asserted-by":"publisher","unstructured":"Seroussi, Y., Bohnert, F., Zukerman, I.: Personalised rating prediction for new users using latent factor models. In: Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, pp. 47\u201356. Association for Computing Machinery, New York, NY, USA (2011).https:\/\/doi.org\/10.1145\/1995966.1995976","DOI":"10.1145\/1995966.1995976"},{"key":"9429_CR71","doi-asserted-by":"publisher","unstructured":"Smyth, B., McClave, P.: Similarity vs. diversity. In: International Conference on Case-based Reasoning, pp. 347\u2013361 (2001). https:\/\/doi.org\/10.1007\/3-540-44593-5_25 . Springer","DOI":"10.1007\/3-540-44593-5_25"},{"key":"9429_CR72","doi-asserted-by":"publisher","unstructured":"Song, Y., Elkahky, A.M., He, X.: Multi-rate deep learning for temporal recommendation. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 909\u2013912. Association for Computing Machinery, New York, NY, USA (2016). https:\/\/doi.org\/10.1145\/2911451.2914726","DOI":"10.1145\/2911451.2914726"},{"key":"9429_CR73","doi-asserted-by":"publisher","unstructured":"Steck, H.: Calibrated recommendations. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 154\u2013162. Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3240323.3240372","DOI":"10.1145\/3240323.3240372"},{"issue":"3","key":"9429_CR74","first-page":"333","volume":"29","author":"GH Stempel","year":"1952","unstructured":"Stempel, G.H.: Sample size for classifying subject matter in dailies. Journalism and Mass Communication Quarterly 29(3), 333 (1952)","journal-title":"Journalism and Mass Communication Quarterly"},{"key":"9429_CR75","volume-title":"Nudge","author":"RH Thaler","year":"2008","unstructured":"Thaler, R.H., Sunstein, C.R.: Nudge. Yale University Press, New Haven, CT and London (2008)"},{"key":"9429_CR76","doi-asserted-by":"publisher","unstructured":"Tintarev, N., Sullivan, E., Guldin, D., Qiu, S., Odjik, D.: Same, same, but different: algorithmic diversification of viewpoints in news. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 7\u201313. Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3213586.3226203","DOI":"10.1145\/3213586.3226203"},{"key":"9429_CR77","doi-asserted-by":"publisher","unstructured":"Van\u00a0Rookhuijzen, M., De\u00a0Vet, E., Adriaanse, M.A.: The effects of nudges: One-shot only? exploring the temporal spillover effects of a default nudge. Frontiers in Psychology 12, 683262 (2021) https:\/\/doi.org\/10.3389\/fpsyg.2021.683262","DOI":"10.3389\/fpsyg.2021.683262"},{"key":"9429_CR78","doi-asserted-by":"publisher","unstructured":"Vargas, S., Castells, P., Vallet, D.: Intent-oriented diversity in recommender systems. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1211\u20131212. Association for Computing Machinery, New York, NY, USA (2011). https:\/\/doi.org\/10.1145\/2009916.2010124","DOI":"10.1145\/2009916.2010124"},{"key":"9429_CR79","doi-asserted-by":"publisher","unstructured":"Venkatesh, V., Thong, J., Xu, X.: Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 36, 157\u2013178 (2012) https:\/\/doi.org\/10.2307\/41410412","DOI":"10.2307\/41410412"},{"key":"9429_CR80","doi-asserted-by":"publisher","unstructured":"Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., Guo, M.: Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 417\u2013426. Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3269206.3271739","DOI":"10.1145\/3269206.3271739"},{"key":"9429_CR81","doi-asserted-by":"publisher","unstructured":"Weinmann, M., Schneider, C., Brocke, J.v.: Digital nudging. Business & Information Systems Engineering 58(6), 433\u2013436 (2016) https:\/\/doi.org\/10.1007\/s12599-016-0453-1","DOI":"10.1007\/s12599-016-0453-1"},{"key":"9429_CR82","doi-asserted-by":"publisher","unstructured":"Weng, J., Miao, C., Goh, A., Shen, Z., Gay, R.: Trust-based agent community for collaborative recommendation. In: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1260\u20131262. Association for Computing Machinery, New York, NY, USA (2006).https:\/\/doi.org\/10.1145\/1160633.1160860","DOI":"10.1145\/1160633.1160860"},{"key":"9429_CR83","doi-asserted-by":"publisher","unstructured":"Yang, J.: Effects of popularity-based news recommendations (\u201cmost-viewed\u201d) on users\u2019 exposure to online news. Media Psychology 19(2), 243\u2013271 (2016) https:\/\/doi.org\/10.1080\/15213269.2015.1006333","DOI":"10.1080\/15213269.2015.1006333"},{"key":"9429_CR84","unstructured":"Zadrozny, B., Elkan, C.P.: Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers. In: International Conference on Machine Learning. Association for Computing Machinery, New York, NY, USA (2001). https:\/\/api.semanticscholar.org\/CorpusID:9594071"},{"key":"9429_CR85","doi-asserted-by":"publisher","unstructured":"Zhai, C., Cohen, W.W., Lafferty, J.: Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In: ACM SIGIR Forum, vol. 49, pp. 2\u20139. Association for Computing Machinery, New York, NY, USA (2015). https:\/\/doi.org\/10.1145\/860435.860440 . ACM New York, NY, USA","DOI":"10.1145\/860435.860440"},{"key":"9429_CR86","doi-asserted-by":"publisher","unstructured":"Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 123\u2013130. Association for Computing Machinery, New York, NY, USA (2008). https:\/\/doi.org\/10.1145\/1454008.1454030","DOI":"10.1145\/1454008.1454030"},{"issue":"2","key":"9429_CR87","doi-asserted-by":"publisher","first-page":"28002","DOI":"10.1209\/0295-5075\/92\/28002","volume":"92","author":"Z-K Zhang","year":"2010","unstructured":"Zhang, Z.-K., Liu, C., Zhang, Y.-C., Zhou, T.: Solving the cold-start problem in recommender systems with social tags. EPL (Europhysics Letters) 92(2), 28002 (2010). https:\/\/doi.org\/10.1209\/0295-5075\/92\/28002","journal-title":"EPL (Europhysics Letters)"},{"key":"9429_CR88","doi-asserted-by":"publisher","unstructured":"Zheng, G., Zhang, F., Zheng, Z., Xiang, Y., Yuan, N.J., Xie, X., Li, Z.: Drn: A deep reinforcement learning framework for news recommendation. In: Proceedings of the 2018 World Wide Web Conference, pp. 167\u2013176. Association for Computing Machinery, New York, NY, USA (2018). https:\/\/doi.org\/10.1145\/3178876.3185994","DOI":"10.1145\/3178876.3185994"},{"key":"9429_CR89","doi-asserted-by":"publisher","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. Association for Computing Machinery, New York, NY, USA (2005). https:\/\/doi.org\/10.1145\/1060745.1060754","DOI":"10.1145\/1060745.1060754"},{"key":"9429_CR90","doi-asserted-by":"publisher","unstructured":"Zolaktaf, Z., Babanezhad, R., Pottinger, R.: A generic top-n recommendation framework for trading-off accuracy, novelty, and coverage. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE), pp. 149\u2013160 (2018). https:\/\/doi.org\/10.1109\/ICDE.2018.00023. IEEE","DOI":"10.1109\/ICDE.2018.00023"}],"container-title":["User Modeling and User-Adapted Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-025-09429-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11257-025-09429-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-025-09429-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T12:04:12Z","timestamp":1751285052000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11257-025-09429-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,25]]},"references-count":90,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["9429"],"URL":"https:\/\/doi.org\/10.1007\/s11257-025-09429-1","relation":{},"ISSN":["0924-1868","1573-1391"],"issn-type":[{"type":"print","value":"0924-1868"},{"type":"electronic","value":"1573-1391"}],"subject":[],"published":{"date-parts":[[2025,3,25]]},"assertion":[{"value":"31 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This paper or a similar version is not currently under review by a journal or conference. This paper is void of plagiarism or self-plagiarism as defined by the Committee on Publication Ethics and Springer Guidelines.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"8"}}