{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T17:15:45Z","timestamp":1767633345211,"version":"3.48.0"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001824","name":"Grantov\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["25-16785S"],"award-info":[{"award-number":["25-16785S"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001824","name":"Grantov\u00e1 Agentura \u010cesk\u00e9 Republiky","doi-asserted-by":"publisher","award":["25-16785S"],"award-info":[{"award-number":["25-16785S"]}],"id":[{"id":"10.13039\/501100001824","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007543","name":"Grantov\u00e1 Agentura, Univerzita Karlova","doi-asserted-by":"publisher","award":["188322"],"award-info":[{"award-number":["188322"]}],"id":[{"id":"10.13039\/100007543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007543","name":"Grantov\u00e1 Agentura, Univerzita Karlova","doi-asserted-by":"publisher","award":["188322"],"award-info":[{"award-number":["188322"]}],"id":[{"id":"10.13039\/100007543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007397","name":"Univerzita Karlova v Praze","doi-asserted-by":"publisher","award":["SVV-260698"],"award-info":[{"award-number":["SVV-260698"]}],"id":[{"id":"10.13039\/100007397","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-025-02642-9","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:45:02Z","timestamp":1767617102000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Long-term fairness in sequential group recommendations"],"prefix":"10.1007","volume":"68","author":[{"given":"Patrik","family":"Dokoupil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ladislav","family":"Peska","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"2642_CR1","doi-asserted-by":"publisher","unstructured":"Serbos D, Qi S, Mamoulis N, Pitoura E, Tsaparas P (2017) Fairness in package-to-group recommendations. In: Proceedings of the 26th international conference on world wide web. WWW \u201917. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, pp 371\u2013379. https:\/\/doi.org\/10.1145\/3038912.3052612","DOI":"10.1145\/3038912.3052612"},{"key":"2642_CR2","doi-asserted-by":"publisher","unstructured":"Sacharidis D (2019) Top-n group recommendations with fairness. In: Proceedings of the 34th ACM\/SIGAPP symposium on applied computing. SAC \u201919. Association for Computing Machinery, New York, NY, USA, pp 1663\u20131670. https:\/\/doi.org\/10.1145\/3297280.3297442","DOI":"10.1145\/3297280.3297442"},{"key":"2642_CR3","doi-asserted-by":"publisher","unstructured":"Serbos D, Qi S, Mamoulis N, Pitoura E, Tsaparas P (2017) Fairness in package-to-group recommendations. In: Proceedings of the 26th international conference on world wide web. WWW \u201917. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, pp 371\u2013379. https:\/\/doi.org\/10.1145\/3038912.3052612","DOI":"10.1145\/3038912.3052612"},{"key":"2642_CR4","doi-asserted-by":"publisher","unstructured":"Xiao L, Min Z, Yongfeng Z, Zhaoquan G, Yiqun L, Shaoping M (2017) Fairness-aware group recommendation with pareto-efficiency. In: Proceedings of the eleventh ACM conference on recommender systems. RecSys \u201917. Association for Computing Machinery, New York, NY, USA, pp 107\u2013115. https:\/\/doi.org\/10.1145\/3109859.3109887","DOI":"10.1145\/3109859.3109887"},{"key":"2642_CR5","doi-asserted-by":"publisher","unstructured":"Malecek L, Peska L (2021) Fairness-preserving group recommendations with user weighting. In: Adjunct proceedings of the 29th ACM conference on user modeling, adaptation and personalization. UMAP \u201921, pp. 4\u20139. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3450614.3461679","DOI":"10.1145\/3450614.3461679"},{"key":"2642_CR6","doi-asserted-by":"publisher","unstructured":"Kaya M, Bridge D, Tintarev N (2020) Ensuring fairness in group recommendations by rank-sensitive balancing of relevance. In: Proceedings of the 14th ACM conference on recommender systems. RecSys \u201920. Association for Computing Machinery, New York, NY, USA, pp 101\u2013110. https:\/\/doi.org\/10.1145\/3383313.3412232","DOI":"10.1145\/3383313.3412232"},{"key":"2642_CR7","doi-asserted-by":"publisher","unstructured":"Stratigi M, Nummenmaa J, Pitoura E, Stefanidis K (2020) Fair sequential group recommendations. In: Proceedings of the 35th annual ACM symposium on applied computing. SAC \u201920. Association for Computing Machinery, New York, NY, USA, pp 1443\u20131452. https:\/\/doi.org\/10.1145\/3341105.3375766","DOI":"10.1145\/3341105.3375766"},{"key":"2642_CR8","doi-asserted-by":"publisher","unstructured":"Heiska I, Stefanidis K (2021) Multi-round recommendations for stable groups. In: 2021 IEEE international conference on progress in informatics and computing (PIC), pp 232\u2013240. https:\/\/doi.org\/10.1109\/PIC53636.2021.9687062","DOI":"10.1109\/PIC53636.2021.9687062"},{"issue":"2","key":"2642_CR9","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s10844-021-00652-x","volume":"58","author":"M Stratigi","year":"2022","unstructured":"Stratigi M, Pitoura E, Nummenmaa J, Stefanidis K (2022) Sequential group recommendations based on satisfaction and disagreement scores. J Intell Inf Syst 58(2):227\u2013254. https:\/\/doi.org\/10.1007\/s10844-021-00652-x","journal-title":"J Intell Inf Syst"},{"issue":"1","key":"2642_CR10","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s11257-023-09366-x","volume":"34","author":"N Hazrati","year":"2023","unstructured":"Hazrati N, Ricci F (2023) Choice models and recommender systems effects on users\u2019 choices. User Model User-Adap Inter 34(1):109\u2013145. https:\/\/doi.org\/10.1007\/s11257-023-09366-x","journal-title":"User Model User-Adap Inter"},{"key":"2642_CR11","doi-asserted-by":"publisher","unstructured":"Hazrati N, Ricci F (2022) Simulating users\u2019 interactions with recommender systems. In: Adjunct Proceedings of the 30th ACM conference on user modeling, adaptation and personalization. UMAP \u201922 Adjunct. Association for Computing Machinery, New York, NY, USA, pp 95\u201398. https:\/\/doi.org\/10.1145\/3511047.3536402","DOI":"10.1145\/3511047.3536402"},{"key":"2642_CR12","doi-asserted-by":"publisher","unstructured":"Ferraro A, Ekstrand MD, Bauer C (2024) It\u2019s not you, it\u2019s me: The impact of choice models and ranking strategies on gender imbalance in music recommendation. In: Proceedings of the 18th ACM conference on recommender systems. RecSys \u201924. Association for Computing Machinery, New York, NY, USA, pp 884\u2013889. https:\/\/doi.org\/10.1145\/3640457.3688163","DOI":"10.1145\/3640457.3688163"},{"key":"2642_CR13","doi-asserted-by":"publisher","unstructured":"Ekstrand MD, Chaney A, Castells P, Burke R, Rohde D, Slokom M (2021) Simurec: Workshop on synthetic data and simulation methods for recommender systems research. In: Proceedings of the 15th ACM conference on recommender systems. RecSys \u201921. Association for Computing Machinery, New York, NY, USA, pp 803\u2013805. https:\/\/doi.org\/10.1145\/3460231.3470938","DOI":"10.1145\/3460231.3470938"},{"key":"2642_CR14","unstructured":"Peska L, Malecek L (2021) Coupled or decoupled evaluation for group recommendation methods? In: Zangerle E, Bauer C, Said A (eds) Proceedings of the perspectives on the evaluation of recommender systems workshop 2021 co-located with the 15th ACM conference on recommender systems (RecSys 2021), Amsterdam, The Netherlands, September 25, 2021. CEUR Workshop Proceedings, vol. 2955. CEUR-WS.org. https:\/\/ceur-ws.org\/Vol-2955\/paper1.pdf"},{"key":"2642_CR15","doi-asserted-by":"publisher","unstructured":"Cao D, He X, Miao L, An Y, Yang C, Hong R (2018) Attentive group recommendation. In: The 41st International ACM SIGIR conference on research & development in information retrieval. SIGIR \u201918. Association for Computing Machinery, New York, NY, USA, pp 645\u2013654. https:\/\/doi.org\/10.1145\/3209978.3209998","DOI":"10.1145\/3209978.3209998"},{"key":"2642_CR16","doi-asserted-by":"publisher","unstructured":"He Z, Chow C-Y, Zhang J-D (2020) Game: Learning graphical and attentive multi-view embeddings for occasional group recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. SIGIR \u201920. Association for Computing Machinery, New York, NY, USA, pp 649\u2013658. https:\/\/doi.org\/10.1145\/3397271.3401064","DOI":"10.1145\/3397271.3401064"},{"key":"2642_CR17","doi-asserted-by":"publisher","unstructured":"He Z, Chow C-Y, Zhang J-D, Li N (2019) Gradi: towards group recommendation using attentive dual top-down and bottom-up influences. In: 2019 IEEE international conference on big data (big data), pp 631\u2013636. https:\/\/doi.org\/10.1109\/BigData47090.2019.9005686","DOI":"10.1109\/BigData47090.2019.9005686"},{"issue":"3","key":"2642_CR18","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1109\/TKDE.2019.2936475","volume":"33","author":"D Cao","year":"2021","unstructured":"Cao D, He X, Miao L, Xiao G, Chen H, Xu J (2021) Social-enhanced attentive group recommendation. IEEE Trans Knowl Data Eng 33(3):1195\u20131209. https:\/\/doi.org\/10.1109\/TKDE.2019.2936475","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2642_CR19","doi-asserted-by":"publisher","unstructured":"Masthoff J, Deli\u0107 A (2022) In: Ricci F, Rokach L, Shapira B (eds) Group recommender systems: beyond preference aggregation. Springer, New York, pp 381\u2013420. https:\/\/doi.org\/10.1007\/978-1-0716-2197-4_10","DOI":"10.1007\/978-1-0716-2197-4_10"},{"key":"2642_CR20","doi-asserted-by":"publisher","unstructured":"Sankar A, Wu Y, Wu Y, Zhang W, Yang H, Sundaram H (2020) Groupim: A mutual information maximization framework for neural group recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. SIGIR \u201920. Association for Computing Machinery, New York, NY, USA, pp 1279\u20131288. https:\/\/doi.org\/10.1145\/3397271.3401116","DOI":"10.1145\/3397271.3401116"},{"key":"2642_CR21","doi-asserted-by":"publisher","unstructured":"Delic A, Neidhardt J, Nguyen TN, Ricci F, Rook L, Werthner H, Zanker M (2016) Observing group decision making processes. In: Proceedings of the 10th ACM conference on recommender systems. RecSys \u201916. Association for Computing Machinery, New York, NY, USA, pp 147\u2013150. https:\/\/doi.org\/10.1145\/2959100.2959168","DOI":"10.1145\/2959100.2959168"},{"key":"2642_CR22","doi-asserted-by":"publisher","unstructured":"Delic A, Masthoff J, Neidhardt J, Werthner H (2018) How to use social relationships in group recommenders: Empirical evidence. In: Proceedings of the 26th conference on user modeling, adaptation and personalization. UMAP \u201918. Association for Computing Machinery, New York, NY, USA, pp 121\u2013129. https:\/\/doi.org\/10.1145\/3209219.3209226","DOI":"10.1145\/3209219.3209226"},{"key":"2642_CR23","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.ins.2022.09.058","volume":"614","author":"Y Leng","year":"2022","unstructured":"Leng Y, Yu L, Niu X (2022) Dynamically aggregating individuals\u2019 social influence and interest evolution for group recommendations. Inf Sci 614:223\u2013239. https:\/\/doi.org\/10.1016\/j.ins.2022.09.058","journal-title":"Inf Sci"},{"key":"2642_CR24","doi-asserted-by":"publisher","unstructured":"Sato R (2022) Enumerating fair packages for group recommendations. In: Proceedings of the Fifteenth ACM international conference on web search and data mining. WSDM \u201922. Association for Computing Machinery, New York, NY, USA, pp 870\u2013878. https:\/\/doi.org\/10.1145\/3488560.3498432","DOI":"10.1145\/3488560.3498432"},{"key":"2642_CR25","doi-asserted-by":"publisher","unstructured":"Dokoupil P (2022) Long-term fairness for group recommender systems with large groups. In: Proceedings of the 16th ACM conference on recommender systems. RecSys \u201922. Association for Computing Machinery, New York, NY, USA, pp 724\u2013726. https:\/\/doi.org\/10.1145\/3523227.3547424","DOI":"10.1145\/3523227.3547424"},{"issue":"1","key":"2642_CR26","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11257-023-09364-z","volume":"34","author":"Y Deldjoo","year":"2023","unstructured":"Deldjoo Y, Jannach D, Bellogin A, Difonzo A, Zanzonelli D (2023) Fairness in recommender systems: research landscape and future directions. User Model User-Adap Inter 34(1):59\u2013108. https:\/\/doi.org\/10.1007\/s11257-023-09364-z","journal-title":"User Model User-Adap Inter"},{"key":"2642_CR27","doi-asserted-by":"publisher","unstructured":"Peska L, Dokoupil P (2022) Towards results-level proportionality for multi-objective recommender systems. In: Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval. SIGIR \u201922. Association for Computing Machinery, New York, NY, USA, pp 1963\u20131968. https:\/\/doi.org\/10.1145\/3477495.3531787","DOI":"10.1145\/3477495.3531787"},{"issue":"3","key":"2642_CR28","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s11257-006-9008-3","volume":"16","author":"J Masthoff","year":"2006","unstructured":"Masthoff J, Gatt A (2006) In pursuit of satisfaction and the prevention of embarrassment: affective state in group recommender systems. User Model User-Adap Inter 16(3):281\u2013319. https:\/\/doi.org\/10.1007\/s11257-006-9008-3","journal-title":"User Model User-Adap Inter"},{"key":"2642_CR29","unstructured":"Collins A, Tkaczyk D, Aizawa A, Beel J (2018) A study of position bias in digital library recommender systems. https:\/\/arxiv.org\/abs\/1802.06565"},{"key":"2642_CR30","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1007\/978-3-319-06028-6_67","volume-title":"Advances in information retrieval","author":"K Hofmann","year":"2014","unstructured":"Hofmann K, Schuth A, Bellog\u00edn A, Rijke M (2014) Effects of position bias on click-based recommender evaluation. In: Rijke M, Kenter T, Vries AP, Zhai C, Jong F, Radinsky K, Hofmann K (eds) Advances in information retrieval. Springer, Cham, pp 624\u2013630"},{"key":"2642_CR31","doi-asserted-by":"publisher","unstructured":"Joachims T, Granka L, Pan B, Hembrooke H, Gay G (2005) Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval. SIGIR \u201905. Association for Computing Machinery, New York, NY, USA, pp 154\u2013161. https:\/\/doi.org\/10.1145\/1076034.1076063","DOI":"10.1145\/1076034.1076063"},{"key":"2642_CR32","doi-asserted-by":"publisher","unstructured":"Chaney A (2021) Recommendation system simulations: a discussion of two key challenges. https:\/\/doi.org\/10.48550\/arXiv.2109.02475","DOI":"10.48550\/arXiv.2109.02475"},{"issue":"1","key":"2642_CR33","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s11238-007-9069-8","volume":"67","author":"DK Herreiner","year":"2009","unstructured":"Herreiner DK, Puppe CD (2009) Envy freeness in experimental fair division problems. Theor Decis 67(1):65\u2013100. https:\/\/doi.org\/10.1007\/s11238-007-9069-8","journal-title":"Theor Decis"},{"key":"2642_CR34","doi-asserted-by":"publisher","unstructured":"Steck H (2019) Embarrassingly shallow autoencoders for sparse data. In: The world wide web conference. WWW \u201919. Association for Computing Machinery, New York, NY, USA, pp 3251\u20133257. https:\/\/doi.org\/10.1145\/3308558.3313710","DOI":"10.1145\/3308558.3313710"},{"key":"2642_CR35","doi-asserted-by":"publisher","unstructured":"Dokoupil P, Peska L (2023) The effect of similarity metric and group size on outlier selection & satisfaction in group recommender systems. In: Adjunct proceedings of the 31st ACM conference on user modeling, adaptation and personalization. UMAP \u201923 Adjunct. Association for Computing Machinery, New York, NY, USA, pp 296\u2013301. https:\/\/doi.org\/10.1145\/3563359.3597386","DOI":"10.1145\/3563359.3597386"},{"key":"2642_CR36","doi-asserted-by":"publisher","unstructured":"Yang L, Cui Y, Xuan Y, Wang C, Belongie S, Estrin D (2018) Unbiased offline recommender evaluation for missing-not-at-random implicit feedback. In: Proceedings of the 12th ACM conference on recommender systems. RecSys \u201918. Association for Computing Machinery, New York, NY, USA, pp 279\u2013287. https:\/\/doi.org\/10.1145\/3240323.3240355","DOI":"10.1145\/3240323.3240355"},{"key":"2642_CR37","doi-asserted-by":"publisher","unstructured":"Dokoupil P, Peska L (2022) Robustness against polarity bias in decoupled group recommendations evaluation. In: Adjunct proceedings of the 30th ACM conference on user modeling, adaptation and personalization. UMAP \u201922 adjunct. Association for Computing Machinery, New York, NY, USA, pp 302\u2013307. https:\/\/doi.org\/10.1145\/3511047.3537650","DOI":"10.1145\/3511047.3537650"},{"key":"2642_CR38","doi-asserted-by":"publisher","unstructured":"Dokoupil P, Peska L (2025) Effects of quantitative explanations on fairness perception in group recommender systems. In: Proceedings of the 33rd ACM conference on user modeling, adaptation and personalization. UMAP \u201925. Association for Computing Machinery, New York, NY, USA, pp 285\u2013289. https:\/\/doi.org\/10.1145\/3699682.3728335","DOI":"10.1145\/3699682.3728335"},{"issue":"2","key":"2642_CR39","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/s10115-017-1082-9","volume":"54","author":"S Qi","year":"2018","unstructured":"Qi S, Mamoulis N, Pitoura E, Tsaparas P (2018) Recommending packages with validity constraints to groups of users. Knowl Inf Syst 54(2):345\u2013374. https:\/\/doi.org\/10.1007\/s10115-017-1082-9","journal-title":"Knowl Inf Syst"},{"key":"2642_CR40","doi-asserted-by":"publisher","unstructured":"Masthoff J (2004) Group modeling: selecting a sequence of television items to suit a group of viewers. Springer, Dordrecht, pp 93\u2013141. https:\/\/doi.org\/10.1007\/1-4020-2164-X_5","DOI":"10.1007\/1-4020-2164-X_5"},{"issue":"1","key":"2642_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11257-023-09363-0","volume":"34","author":"F Barile","year":"2024","unstructured":"Barile F, Draws T, Inel O, Rieger A, Najafian S, Ebrahimi Fard A, Hada R, Tintarev N (2024) Evaluating explainable social choice-based aggregation strategies for group recommendation. User Model User-Adap Inter 34(1):1\u201358. https:\/\/doi.org\/10.1007\/s11257-023-09363-0","journal-title":"User Model User-Adap Inter"},{"key":"2642_CR42","first-page":"388","volume-title":"Group dynamics","author":"DR Forsyth","year":"2006","unstructured":"Forsyth DR (2006) Conflict. In: Forsyth DR (ed) Group dynamics, 5th edn. Wadsworth Cengage Learning, Belmont, pp 388\u2013389","edition":"5"},{"issue":"3","key":"2642_CR43","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1016\/j.ejor.2016.08.013","volume":"257","author":"G Nicosia","year":"2017","unstructured":"Nicosia G, Pacifici A, Pferschy U (2017) Price of fairness for allocating a bounded resource. Eur J Oper Res 257(3):933\u2013943. https:\/\/doi.org\/10.1016\/j.ejor.2016.08.013","journal-title":"Eur J Oper Res"},{"issue":"8","key":"2642_CR44","doi-asserted-by":"publisher","first-page":"11293","DOI":"10.1007\/s11063-023-11376-0","volume":"55","author":"Y Liang","year":"2023","unstructured":"Liang Y (2023) Dfgr: Diversity and fairness awareness of group recommendation in an event-based social network. Neural Process Lett 55(8):11293\u201311312. https:\/\/doi.org\/10.1007\/s11063-023-11376-0","journal-title":"Neural Process Lett"},{"key":"2642_CR45","doi-asserted-by":"publisher","unstructured":"Mansoury M, Burke R, Mobasher B (2021) Flatter is better: percentile transformations for recommender systems. ACM Trans Intell Syst Technol 12(2) https:\/\/doi.org\/10.1145\/3437910","DOI":"10.1145\/3437910"},{"key":"2642_CR46","doi-asserted-by":"publisher","unstructured":"Park S, Yoon M, Kim H-y, Lee J (2025) Why is normalization necessary for linear recommenders? In: Proceedings of the 48th international ACM SIGIR conference on research and development in information retrieval. SIGIR \u201925. Association for Computing Machinery, New York, NY, USA, pp 2142\u20132151. https:\/\/doi.org\/10.1145\/3726302.3730116","DOI":"10.1145\/3726302.3730116"},{"key":"2642_CR47","doi-asserted-by":"publisher","unstructured":"Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval. SIGIR \u201999. Association for Computing Machinery, New York, NY, USA, pp 230\u2013237. https:\/\/doi.org\/10.1145\/312624.312682","DOI":"10.1145\/312624.312682"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02642-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02642-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02642-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T12:45:05Z","timestamp":1767617105000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02642-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,5]]},"references-count":47,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2642"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02642-9","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,5]]},"assertion":[{"value":"15 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 October 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 January 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"37"}}