{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T13:23:22Z","timestamp":1770902602261,"version":"3.50.1"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008905","name":"University of Klagenfurt","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008905","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["User Model User-Adap Inter"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The diversity of the generated item suggestions can be an important quality factor of a recommender system. In offline experiments, diversity is commonly assessed with the help of the <jats:italic>intra-list similarity<\/jats:italic> (ILS) measure, which is defined as the average pairwise similarity of the items in a list. The similarity of each pair of items is often determined based on domain-specific meta-data, e.g., movie genres. While this approach is common in the literature, it in most cases remains open if a particular implementation of the ILS measure is actually a valid proxy for the human diversity perception in a given application. With this work, we address this research gap and investigate the correlation of different ILS implementations with human perceptions in the domains of movie and recipe recommendation. We conducted several user studies involving over 500 participants. Our results indicate that the particularities of the ILS metric implementation matter. While we found that the ILS metric <jats:italic>can<\/jats:italic> be a good proxy for human perceptions, it turns out that it is important to individually validate the used ILS metric implementation for a given application. On a more general level, our work points to a certain level of oversimplification in recommender systems research when it comes to the design of computational proxies for human quality perceptions and thus calls for more research regarding the validation of the corresponding metrics.<\/jats:p>","DOI":"10.1007\/s11257-022-09351-w","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T04:12:27Z","timestamp":1670818347000},"page":"769-802","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Intra-list similarity and human diversity perceptions of recommendations: the details matter"],"prefix":"10.1007","volume":"33","author":[{"given":"Mathias","family":"Jesse","sequence":"first","affiliation":[]},{"given":"Christine","family":"Bauer","sequence":"additional","affiliation":[]},{"given":"Dietmar","family":"Jannach","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"9351_CR1","unstructured":"Abdollahpouri, H., Burke, R., Mobasher, B.: Managing popularity bias in recommender systems with personalized re-ranking, pp. 1\u20136. ArXiv, arXiv:1901.07555 (2019)"},{"issue":"5","key":"9351_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":"9351_CR3","volume-title":"The Theory of Committees and Elections","author":"D Black","year":"1958","unstructured":"Black, D.: The Theory of Committees and Elections. Springer, New York (1958)"},{"key":"9351_CR4","unstructured":"Bradley, K., Smyth, B.: Improving recommendation diversity. In: Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, pp. 85\u201394 (2001)"},{"key":"9351_CR5","doi-asserted-by":"publisher","unstructured":"Brovman, Y.M., Jacob, M., Srinivasan, N., Neola, S., Galron, D., Snyder, R., Wang, P.: Optimizing similar item recommendations in a semi-structured marketplace to maximize conversion. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 199\u2013202 (2016). https:\/\/doi.org\/10.1145\/2959100.2959166","DOI":"10.1145\/2959100.2959166"},{"key":"9351_CR6","doi-asserted-by":"publisher","unstructured":"Chen, L., Wu, W., He, L.: How personality influences users\u2019 needs for recommendation diversity? In: CHI \u201913 Extended Abstracts on Human Factors in Computing Systems, pp. 829\u2013834 (2013). https:\/\/doi.org\/10.1145\/2468356.2468505","DOI":"10.1145\/2468356.2468505"},{"key":"9351_CR7","doi-asserted-by":"publisher","unstructured":"Clarke, C.L., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., B\u00fcttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 659\u2013666 (2008). https:\/\/doi.org\/10.1145\/1390334.1390446","DOI":"10.1145\/1390334.1390446"},{"key":"9351_CR8","doi-asserted-by":"publisher","unstructured":"Colucci, L., Doshi, P., Lee, K.L., Liang, J., Lin, Y., Vashishtha, I., Zhang, J., Jude, A.: Evaluating item-item similarity algorithms for movies. In: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2141\u20132147 (2016). https:\/\/doi.org\/10.1145\/2851581.2892362","DOI":"10.1145\/2851581.2892362"},{"key":"9351_CR9","doi-asserted-by":"publisher","unstructured":"de\u00a0Gemmis, M., Lops, P., Musto, C., Narducci, F., Semeraro, G.: Semantics-aware content-based recommender systems. In: Recommender Systems Handbook, pp. 119\u2013159 (2015). https:\/\/doi.org\/10.1007\/978-1-4899-7637-6_4","DOI":"10.1007\/978-1-4899-7637-6_4"},{"key":"9351_CR10","doi-asserted-by":"publisher","unstructured":"Deldjoo, Y., Jannach, D., Bellogin, A., Difonzo, A., Zanzonelli, D.: A survey of research on fair recommender systems (2022). https:\/\/doi.org\/10.48550\/ARXIV.2205.11127","DOI":"10.48550\/ARXIV.2205.11127"},{"key":"9351_CR11","doi-asserted-by":"publisher","unstructured":"Downie, J.S., Lee, J.H., Gruzd, A.A., Jones, M.C.: Toward an understanding of similarity judgments for music digital library evaluation. In: Proceedings of the 7th ACM\/IEEE-CS Joint Conference on Digital Libraries, pp. 307\u2013308 (2007). https:\/\/doi.org\/10.1145\/1255175.1255235","DOI":"10.1145\/1255175.1255235"},{"issue":"6","key":"9351_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102721","volume":"58","author":"Y Du","year":"2021","unstructured":"Du, Y., Ranwez, S., Sutton-Charani, N., Ranwez, V.: Is diversity optimization always suitable? Toward a better understanding of diversity within recommendation approaches. Inf. Process. Manag. 58(6), 102721 (2021). https:\/\/doi.org\/10.1016\/j.ipm.2021.102721","journal-title":"Inf. Process. Manag."},{"key":"9351_CR13","doi-asserted-by":"publisher","unstructured":"Ekstrand, M.D., Harper, F.M., Willemsen, M.C., Konstan, J.A.: User perception of differences in recommender algorithms. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 161\u2013168 (2014). https:\/\/doi.org\/10.1145\/2645710.2645737","DOI":"10.1145\/2645710.2645737"},{"key":"9351_CR14","unstructured":"Ellis, D.P., Whitman, B., Berenzweig, A., Lawrence, S.: The quest for ground truth in musical artist similarity. In: Proceedings of the 3rd International Conference on Music Information Retrieval (2002). https:\/\/ismir2002.ismir.net\/proceedings\/02-FP05-4.pdf"},{"key":"9351_CR15","doi-asserted-by":"publisher","unstructured":"Fleder, D.M., Hosanagar, K.: Recommender systems and their impact on sales diversity. In: Proceedings of the 8th ACM Conference on Electronic Commerce, pp. 192\u2013199 (2007). https:\/\/doi.org\/10.1145\/1250910.1250939","DOI":"10.1145\/1250910.1250939"},{"key":"9351_CR16","unstructured":"Ge, M., Gedikli, F., Jannach, D.: Placing high-diversity items in top-n recommendation lists. In: Proceedings of the Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2011 at IJCAI 2011) (2011)"},{"key":"9351_CR17","doi-asserted-by":"publisher","unstructured":"Ge, M., Jannach, D., Gedikli, F., Hepp, M.: Effects of the placement of diverse items in recommendation lists. In: 14th International Conference on Enterprise Information Systems, pp. 201\u2013208 (2012). https:\/\/doi.org\/10.5220\/0003974802010208","DOI":"10.5220\/0003974802010208"},{"key":"9351_CR18","doi-asserted-by":"publisher","unstructured":"Hauptmann, H., Leipold, N., Madenach, M., Wintergerst, M., Lurz, M., Groh, G., B\u00f6hm, M., Gedrich, K., Krcmar, H.: Effects and challenges of using a nutrition assistance system: results of a long-term mixed-method study. In: User Modeling and User-Adapted Interaction (2021). https:\/\/doi.org\/10.1007\/s11257-021-09301-y","DOI":"10.1007\/s11257-021-09301-y"},{"key":"9351_CR19","doi-asserted-by":"publisher","unstructured":"Jannach, D., Kamehkhosh, I., Lerche, L.: Leveraging multi-dimensional user models for personalized next-track music recommendation. In: Proceedings of the Symposium on Applied Computing, pp. 1635\u20131642 (2017). https:\/\/doi.org\/10.1145\/3019612.3019756","DOI":"10.1145\/3019612.3019756"},{"key":"9351_CR20","unstructured":"Jannach, D.: Multi-objective recommendation: Overview and challenges. In: Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022). arXiv:2210.10309 (2022)"},{"issue":"5","key":"9351_CR21","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 Adapt. Interact. 25(5), 427\u2013491 (2015). https:\/\/doi.org\/10.1007\/s11257-015-9165-3","journal-title":"User Model. User Adapt. Interact."},{"issue":"2\u20133","key":"9351_CR22","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1101\/lm.3.2-3.257","volume":"3","author":"O Jensen","year":"1996","unstructured":"Jensen, O., Lisman, J.E.: Novel lists of 7+\/-2 known items can be reliably stored in an oscillatory short-term memory network: interaction with long-term memory. Learn. Mem. 3(2\u20133), 257\u2013263 (1996). https:\/\/doi.org\/10.1101\/lm.3.2-3.257","journal-title":"Learn. Mem."},{"key":"9351_CR23","doi-asserted-by":"publisher","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 (2016). https:\/\/doi.org\/10.1145\/2926720","DOI":"10.1145\/2926720"},{"key":"9351_CR24","doi-asserted-by":"crossref","unstructured":"Knijnenburg, B.P., Willemsen, M.C., Kobsa, A.: A pragmatic procedure to support the user-centric evaluation of recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 321\u2013324 (2011). https:\/\/doi.org\/10.1145\/2043932.2043993","DOI":"10.1145\/2043932.2043993"},{"key":"9351_CR25","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.knosys.2017.02.009","volume":"123","author":"M Kunaver","year":"2017","unstructured":"Kunaver, M., Po\u017erl, T.: Diversity in recommender systems\u2014a survey. Knowl. Based Syst. 123, 154\u2013162 (2017). https:\/\/doi.org\/10.1016\/j.knosys.2017.02.009","journal-title":"Knowl. Based Syst."},{"key":"9351_CR26","unstructured":"Lee, J.H.: Crowdsourcing music similarity judgments using mechanical turk. In: Proceedings of the 11th International Society for Music Information Retrieval Conference, pp. 183\u2013188 (2010)"},{"key":"9351_CR27","doi-asserted-by":"publisher","unstructured":"Lin, K., Sonboli, N., Mobasher, B., Burke, R.: Calibration in collaborative filtering recommender systems: a user-centered analysis. In: Proceedings of the 31st ACM Conference on Hypertext and Social Media, pp. 197\u2013206, (2020) https:\/\/doi.org\/10.1145\/3372923.3404793","DOI":"10.1145\/3372923.3404793"},{"key":"9351_CR28","doi-asserted-by":"publisher","unstructured":"Mauro, N., Ardissono, L.: Extending a tag-based collaborative recommender with co-occurring information interests. In: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, pp. 181\u2013190 (2019). https:\/\/doi.org\/10.1145\/3320435.3320458","DOI":"10.1145\/3320435.3320458"},{"key":"9351_CR29","doi-asserted-by":"publisher","unstructured":"McGinty, L., Smyth, B.: On the role of diversity in conversational recommender systems. In: Case-Based Reasoning Research and Development, pp. 276\u2013290 (2003). https:\/\/doi.org\/10.1007\/3-540-45006-8_23","DOI":"10.1007\/3-540-45006-8_23"},{"key":"9351_CR30","doi-asserted-by":"publisher","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 (2006). https:\/\/doi.org\/10.1145\/1125451.1125659","DOI":"10.1145\/1125451.1125659"},{"issue":"2","key":"9351_CR31","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1037\/h0043158","volume":"63","author":"GA Miller","year":"1956","unstructured":"Miller, G.A.: The magical number seven: plus or minus two: Some limits on our capacity for processing information. Psychol. Rev. 63(2), 81\u201397 (1956). https:\/\/doi.org\/10.1037\/h0043158","journal-title":"Psychol. Rev."},{"key":"9351_CR32","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.elerap.2016.09.003","volume":"19","author":"M Nilashi","year":"2016","unstructured":"Nilashi, M., Jannach, D., Bin Ibrahim, O., Esfahani, M.D., Ahmadi, H.: Recommendation quality, transparency, and website quality for trust-building in recommendation agents. Electron. Commer. Res. Appl. 19, 70\u201384 (2016). https:\/\/doi.org\/10.1016\/j.elerap.2016.09.003","journal-title":"Electron. Commer. Res. Appl."},{"key":"9351_CR33","doi-asserted-by":"crossref","unstructured":"Porcaro, L., G\u00f3mez, E., Castillo, C.: Perceptions of diversity in electronic music: The impact of listener, artist, and track characteristics. Proc. ACM Hum. Comput. Interact. (2022) https:\/\/doi.org\/10.1145\/3512956","DOI":"10.1145\/3512956"},{"key":"9351_CR34","doi-asserted-by":"publisher","unstructured":"Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 157\u2013164 (2011). https:\/\/doi.org\/10.1145\/2043932.2043962","DOI":"10.1145\/2043932.2043962"},{"key":"9351_CR35","doi-asserted-by":"publisher","unstructured":"Rendle, S., Krichene, W., Zhang, L., Anderson, J.: Neural collaborative filtering vs. matrix factorization revisited. In: Proceedings of the 14th ACM Conference on Recommender Systems, RecSys \u201920, pp. 240\u2013248 (2020). https:\/\/doi.org\/10.1145\/3383313.3412488","DOI":"10.1145\/3383313.3412488"},{"key":"9351_CR36","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Ziviani, N., Moura, E.S.D., Hata, I., Lacerda, A., Veloso, A.: Multiobjective pareto-efficient approaches for recommender systems. ACM Trans. Intell. Syst. Technol. (2015) https:\/\/doi.org\/10.1145\/2629350","DOI":"10.1145\/2629350"},{"key":"9351_CR37","volume-title":"The Coding Manual for Qualitative Researchers","author":"J Saldana","year":"2015","unstructured":"Saldana, J.: The Coding Manual for Qualitative Researchers, 3rd edn. Sage Publications, London (2015)","edition":"3"},{"key":"9351_CR38","doi-asserted-by":"crossref","unstructured":"Shi, Y., Zhao, X., Wang, J., Larson, M., Hanjalic, A.: Adaptive diversification of recommendation results via latent factor portfolio. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 175\u2013184 (2012). https:\/\/doi.org\/10.1145\/2348283.2348310","DOI":"10.1145\/2348283.2348310"},{"key":"9351_CR39","unstructured":"Starke, A.D., \u00d8verhaug, S., Trattner, C.: Predicting feature-based similarity in the news domain using human judgments. In: Proceedings of the 9th International Workshop on News Recommendation and Analytics (2021)"},{"key":"9351_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11257-019-09245-4","volume":"30","author":"C Trattner","year":"2019","unstructured":"Trattner, C., Jannach, D.: Learning to recommend similar items from human judgements. User Model. User Adapt. Interact. 30, 1\u201349 (2019). https:\/\/doi.org\/10.1007\/s11257-019-09245-4","journal-title":"User Model. User Adapt. Interact."},{"key":"9351_CR41","doi-asserted-by":"publisher","unstructured":"Tsai, C.H., Brusilovsky, P.: Beyond the ranked list: User-driven exploration and diversification of social recommendation. In: 23rd International Conference on Intelligent User Interfaces, pp. 239\u2013250 (2018). https:\/\/doi.org\/10.1145\/3172944.3172959","DOI":"10.1145\/3172944.3172959"},{"key":"9351_CR42","doi-asserted-by":"publisher","unstructured":"van Pinxteren, Y., Geleijnse, G., Kamsteeg, P.: Deriving a recipe similarity measure for recommending healthful meals. In: Proceedings of the 16th International Conference on Intelligent User Interfaces, pp. 105\u2013114 (2011). https:\/\/doi.org\/10.1145\/1943403.1943422","DOI":"10.1145\/1943403.1943422"},{"key":"9351_CR43","doi-asserted-by":"publisher","unstructured":"Vargas, S., Baltrunas, L., Karatzoglou, A., Castells, P.: Coverage, redundancy and size-awareness in genre diversity for recommender systems. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 209\u2013216 (2014). https:\/\/doi.org\/10.1145\/2645710.2645743","DOI":"10.1145\/2645710.2645743"},{"key":"9351_CR44","doi-asserted-by":"publisher","unstructured":"Vargas, S., Castells, P., Vallet, D.: Explicit relevance models in intent-oriented information retrieval diversification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 75\u201384 (2012). https:\/\/doi.org\/10.1145\/2348283.2348297","DOI":"10.1145\/2348283.2348297"},{"key":"9351_CR45","doi-asserted-by":"publisher","unstructured":"Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 109\u2013116 (2011). https:\/\/doi.org\/10.1145\/2043932.2043955","DOI":"10.1145\/2043932.2043955"},{"key":"9351_CR46","unstructured":"Vargas, S.: New approaches to diversity and novelty in recommender systems. In: Fourth BCS-IRSG Symposium on Future Directions in Information Access, pp. 8\u201313 (2011). https:\/\/doi.org\/10.5555\/2227322.2227324"},{"key":"9351_CR47","doi-asserted-by":"publisher","unstructured":"Vig, J., Sen, S., Riedl, J.: Tagsplanations: Explaining recommendations using tags. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, pp. 47\u201356 (2009). https:\/\/doi.org\/10.1145\/1502650.1502661","DOI":"10.1145\/1502650.1502661"},{"key":"9351_CR48","doi-asserted-by":"crossref","unstructured":"Wang, C., Agrawal, A., Li, X., Makkad, T., Veljee, E., Mengshoel, O., Jude, A.: Content-based top-n recommendations with perceived similarity. In: Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1052\u20131057 (2017). https:\/\/doi.org\/10.1109\/SMC.2017.8122750","DOI":"10.1109\/SMC.2017.8122750"},{"issue":"4","key":"9351_CR49","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s11257-016-9178-6","volume":"26","author":"MC Willemsen","year":"2016","unstructured":"Willemsen, M.C., Graus, M.P., Knijnenburg, B.P.: Understanding the role of latent feature diversification on choice difficulty and satisfaction. User Model. User Adapt. Interact. 26(4), 347\u2013389 (2016). https:\/\/doi.org\/10.1007\/s11257-016-9178-6","journal-title":"User Model. User Adapt. Interact."},{"key":"9351_CR50","doi-asserted-by":"publisher","unstructured":"Yao, Y., Harper, F.M.: Judging similarity: a user-centric study of related item recommendations. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 288\u2013296 (2018). https:\/\/doi.org\/10.1145\/3240323.3240351","DOI":"10.1145\/3240323.3240351"},{"key":"9351_CR51","doi-asserted-by":"publisher","unstructured":"Zeng, Z., Lin, J., Li, L., Pan, W., Ming, Z.: Next-item recommendation via collaborative filtering with bidirectional item similarity. ACM Trans. Inf. Syst. (2019). https:\/\/doi.org\/10.1145\/3366172","DOI":"10.1145\/3366172"},{"key":"9351_CR52","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.neucom.2021.11.041","volume":"474","author":"Y Zheng","year":"2022","unstructured":"Zheng, Y., Wang, D.X.: A survey of recommender systems with multi-objective optimization. Neurocomputing 474, 141\u2013153 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.11.041","journal-title":"Neurocomputing"},{"key":"9351_CR53","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 (2005). https:\/\/doi.org\/10.1145\/1060745.1060754","DOI":"10.1145\/1060745.1060754"}],"container-title":["User Modeling and User-Adapted Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-022-09351-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11257-022-09351-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-022-09351-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T18:09:47Z","timestamp":1692900587000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11257-022-09351-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,12]]},"references-count":53,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["9351"],"URL":"https:\/\/doi.org\/10.1007\/s11257-022-09351-w","relation":{},"ISSN":["0924-1868","1573-1391"],"issn-type":[{"value":"0924-1868","type":"print"},{"value":"1573-1391","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,12]]},"assertion":[{"value":"20 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}