{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T09:05:07Z","timestamp":1750237507880,"version":"3.37.3"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T00:00:00Z","timestamp":1659657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100013003","name":"Universit\u00e0 degli Studi di Cagliari","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100013003","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":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Offering timely support to users in eCoaching systems is a key factor to keep them engaged. However, coaches usually follow a lot of users, so it is hard for them to prioritize those with whom they should interact first. Timeliness is especially needed when health implications might be the consequence of a lack of support. In this paper, we focus on this last scenario, by considering an eCoaching platform for runners. Our goal is to provide a coach with a ranked list of users, according to the support they need. Moreover, we want to guarantee a fair exposure in the ranking, to make sure that users of different groups have equal opportunities to get supported. In order to do so, we first model their performance and running behavior and then present a ranking algorithm to recommend users to coaches, according to their performance in the last running session and the quality of the previous ones. We provide measures of fairness that allow us to assess the exposure of users of different groups in the ranking and propose a re-ranking algorithm to guarantee a fair exposure. Experiments on data coming from the previously mentioned platform for runners show the effectiveness of our approach on standard metrics for ranking quality assessment and its capability to provide a fair exposure to users. The source code and the preprocessed datasets are available at: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/wiguider\/Fair-Performance-based-User-Recommendation-in-eCoaching-Systems\">https:\/\/github.com\/wiguider\/Fair-Performance-based-User-Recommendation-in-eCoaching-Systems<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/s11257-022-09339-6","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T17:04:29Z","timestamp":1659719069000},"page":"839-881","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fair performance-based user recommendation in eCoaching systems"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6053-3015","authenticated-orcid":false,"given":"Ludovico","family":"Boratto","sequence":"first","affiliation":[]},{"given":"Salvatore","family":"Carta","sequence":"additional","affiliation":[]},{"given":"Walid","family":"Iguider","sequence":"additional","affiliation":[]},{"given":"Fabrizio","family":"Mulas","sequence":"additional","affiliation":[]},{"given":"Paolo","family":"Pilloni","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,5]]},"reference":[{"key":"9339_CR1","doi-asserted-by":"crossref","unstructured":"Ahire, S.B., Khanuja, H.K.: A personalized framework for health care recommendation. In: 2015 International Conference on Computing Communication Control and Automation, pp. 442\u2013445. IEEE (2015)","DOI":"10.1109\/ICCUBEA.2015.92"},{"key":"9339_CR2","doi-asserted-by":"publisher","unstructured":"Amatriain, X., Basilico, J.: Recommender systems in industry: A netflix case study. In: Ricci, F., Rokach, L., Shapira, B. (eds.) Recommender Systems Handbook, pp. 385\u2013419. Springer (2015). https:\/\/doi.org\/10.1007\/978-1-4899-7637-6_11","DOI":"10.1007\/978-1-4899-7637-6_11"},{"issue":"1","key":"9339_CR3","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/1007730.1007735","volume":"6","author":"GE Batista","year":"2004","unstructured":"Batista, G.E., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor. Newsl. 6(1), 20\u201329 (2004)","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"9339_CR4","doi-asserted-by":"publisher","unstructured":"Berndsen, J., Smyth, B., Lawlor, A.: Pace my race: recommendations for marathon running. In: Bogers, T., Said, A., Brusilovsky, P., Tikk, D. (eds.) Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019, pp. 246\u2013250. ACM (2019). https:\/\/doi.org\/10.1145\/3298689.3346991","DOI":"10.1145\/3298689.3346991"},{"key":"9339_CR5","doi-asserted-by":"publisher","unstructured":"Beutel, A., Chen, J., Doshi, T., Qian, H., Wei, L., Wu, Y., Heldt, L., Zhao, Z., Hong, L., Chi, E.H., Goodrow, C.: Fairness in recommendation ranking through pairwise comparisons. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019., pp. 2212\u20132220. ACM (2019). https:\/\/doi.org\/10.1145\/3292500.3330745","DOI":"10.1145\/3292500.3330745"},{"key":"9339_CR6","doi-asserted-by":"publisher","unstructured":"Biega, A.J., Gummadi, K.P., Weikum, G.: Equity of attention: Amortizing individual fairness in rankings. In: Collins-Thompson, K., Mei, Q., Davison, B.D., Liu, Y., Yilmaz, E. (eds.) The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR 2018, Ann Arbor, MI, USA, July 08-12, 2018, pp. 405\u2013414. ACM (2018). https:\/\/doi.org\/10.1145\/3209978.3210063","DOI":"10.1145\/3209978.3210063"},{"key":"9339_CR7","unstructured":"Binns, R.: Fairness in machine learning: Lessons from political philosophy. In: Friedler, S.A., Wilson, C. (eds.) Conference on Fairness, Accountability and Transparency, FAT 2018, 23-24 February 2018, New York, NY, USA, Proceedings of Machine Learning Research, vol.\u00a081, pp. 149\u2013159. PMLR (2018). http:\/\/proceedings.mlr.press\/v81\/binns18a.html"},{"key":"9339_CR8","unstructured":"Boratto, L., Carta, S., Iguider, W., Mulas, F., Pilloni, P.: Predicting workout quality to help coaches support sportspeople. In: Elsweiler, D., Ludwig, B., Said, A., Sch\u00e4fer, H., Torkamaan, H., Trattner, C. (eds.) Proceedings of the 3rd International Workshop on Health Recommender Systems, HealthRecSys 2018, co-located with the 12th ACM Conference on Recommender Systems (ACM RecSys 2018), Vancouver, BC, Canada, October 6, 2018, CEUR Workshop Proceedings, vol. 2216, pp. 8\u201312. CEUR-WS.org (2018). http:\/\/ceur-ws.org\/Vol-2216\/healthRecSys18_paper_2.pdf"},{"issue":"4","key":"9339_CR9","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1007\/s00779-017-1026-0","volume":"21","author":"L Boratto","year":"2017","unstructured":"Boratto, L., Carta, S., Mulas, F., Pilloni, P.: An e-coaching ecosystem: design and effectiveness analysis of the engagement of remote coaching on athletes. Pers. Ubiquit. Comput. 21(4), 689\u2013704 (2017). https:\/\/doi.org\/10.1007\/s00779-017-1026-0","journal-title":"Pers. Ubiquit. Comput."},{"issue":"3","key":"9339_CR10","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 Adapt. Interact. 31(3), 421\u2013455 (2021). https:\/\/doi.org\/10.1007\/s11257-021-09294-8","journal-title":"User Model. User Adapt. Interact."},{"issue":"1","key":"9339_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"issue":"1","key":"9339_CR12","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.aml.2012.03.031","volume":"26","author":"JP Brooks","year":"2013","unstructured":"Brooks, J.P., Dul\u00e1, J.H.: The l1-norm best-fit hyperplane problem. Appl. Math. Lett. 26(1), 51\u201355 (2013)","journal-title":"Appl. Math. Lett."},{"issue":"3","key":"9339_CR13","doi-asserted-by":"publisher","first-page":"3446","DOI":"10.1016\/j.eswa.2011.09.033","volume":"39","author":"I Brown","year":"2012","unstructured":"Brown, I., Mues, C.: An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Syst. Appl. 39(3), 3446\u20133453 (2012)","journal-title":"Expert Syst. Appl."},{"key":"9339_CR14","unstructured":"Burke, R., Sonboli, N., Ordonez-Gauger, A.: Balanced neighborhoods for multi-sided fairness in recommendation. In: Conference on Fairness, Accountability and Transparency, FAT 2018, Proceedings of Machine Learning Research, vol.\u00a081, pp. 202\u2013214. PMLR (2018). http:\/\/proceedings.mlr.press\/v81\/burke18a.html"},{"key":"9339_CR15","doi-asserted-by":"publisher","unstructured":"Carbonell, J.G., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR \u201998: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335\u2013336. ACM (1998). https:\/\/doi.org\/10.1145\/290941.291025","DOI":"10.1145\/290941.291025"},{"key":"9339_CR16","doi-asserted-by":"publisher","unstructured":"Celis, L.E., Straszak, D., Vishnoi, N.K.: Ranking with fairness constraints. In: 45th International Colloquium on Automata, Languages, and Programming, ICALP 2018, LIPIcs, vol. 107, pp. 28:1\u201328:15. Schloss Dagstuhl - Leibniz-Zentrum f\u00fcr Informatik (2018). https:\/\/doi.org\/10.4230\/LIPIcs.ICALP.2018.28","DOI":"10.4230\/LIPIcs.ICALP.2018.28"},{"key":"9339_CR17","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"9339_CR18","doi-asserted-by":"publisher","unstructured":"Dobrican, R., Zampuni\u00e9ris, D.: A proactive solution, using wearable and mobile applications, for closing the gap between the rehabilitation team and cardiac patients. In: 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016, Chicago, IL, USA, October 4-7, 2016, pp. 146\u2013155. IEEE Computer Society (2016). https:\/\/doi.org\/10.1109\/ICHI.2016.23","DOI":"10.1109\/ICHI.2016.23"},{"key":"9339_CR19","doi-asserted-by":"publisher","unstructured":"Donciu, M., Ionita, M., Dascalu, M., Trausan-Matu, S.: The runner - recommender system of workout and nutrition for runners. In: Wang, D., Negru, V., Ida, T., Jebelean, T., Petcu, D., Watt, S.M., Zaharie, D. (eds.) 13th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2011, Timisoara, Romania, September 26-29, 2011, pp. 230\u2013238. IEEE Computer Society (2011). https:\/\/doi.org\/10.1109\/SYNASC.2011.18","DOI":"10.1109\/SYNASC.2011.18"},{"key":"9339_CR20","doi-asserted-by":"publisher","unstructured":"Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.S.: Fairness through awareness. In: Goldwasser, S. (ed.) Innovations in Theoretical Computer Science 2012, Cambridge, MA, USA, January 8-10, 2012, pp. 214\u2013226. ACM (2012). https:\/\/doi.org\/10.1145\/2090236.2090255","DOI":"10.1145\/2090236.2090255"},{"key":"9339_CR21","doi-asserted-by":"publisher","unstructured":"Fenu, G., Lafhouli, H., Marras, M.: Exploring algorithmic fairness in deep speaker verification. In: Gervasi, O., Murgante, B., Misra, S., Garau, C., Blecic, I., Taniar, D., Apduhan, B.O., Rocha, A.M.A.C., Tarantino, E., Torre, C.M., Karaca, Y. (eds.) Computational Science and Its Applications - ICCSA 2020 - 20th International Conference, Cagliari, Italy, July 1-4, 2020, Proceedings, Part IV, Lecture Notes in Computer Science, vol. 12252, pp. 77\u201393. Springer (2020). https:\/\/doi.org\/10.1007\/978-3-030-58811-3_6","DOI":"10.1007\/978-3-030-58811-3_6"},{"issue":"8","key":"9339_CR22","doi-asserted-by":"publisher","first-page":"1761","DOI":"10.1016\/j.patcog.2011.01.017","volume":"44","author":"M Galar","year":"2011","unstructured":"Galar, M., Fern\u00e1ndez, A., Barrenechea, E., Bustince, H., Herrera, F.: An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes. Pattern Recogn. 44(8), 1761\u20131776 (2011)","journal-title":"Pattern Recogn."},{"issue":"1","key":"9339_CR23","doi-asserted-by":"publisher","first-page":"79","DOI":"10.4310\/SII.2018.v11.n1.a7","volume":"11","author":"X Gao","year":"2018","unstructured":"Gao, X., Feng, Y.: Penalized weighted least absolute deviation regression. Stat. Interface 11(1), 79\u201389 (2018)","journal-title":"Stat. Interface"},{"issue":"1","key":"9339_CR24","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3\u201342 (2006)","journal-title":"Mach. Learn."},{"key":"9339_CR25","doi-asserted-by":"publisher","unstructured":"Geyik, S.C., Ambler, S., Kenthapadi, K.: Fairness-aware ranking in search and recommendation systems with application to linkedin talent search. In: Teredesai, A., Kumar, V., Li, Y., Rosales, R., Terzi, E., Karypis, G. (eds.) Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019, pp. 2221\u20132231. ACM (2019). https:\/\/doi.org\/10.1145\/3292500.3330691","DOI":"10.1145\/3292500.3330691"},{"key":"9339_CR26","doi-asserted-by":"publisher","unstructured":"Guy, I., Pizzato, L.: People recommendation tutorial. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 431\u2013432. ACM (2016). https:\/\/doi.org\/10.1145\/2959100.2959196","DOI":"10.1145\/2959100.2959196"},{"key":"9339_CR27","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Lee, D.D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pp. 3315\u20133323 (2016). https:\/\/proceedings.neurips.cc\/paper\/2016\/hash\/9d2682367c3935defcb1f9e247a97c0d-Abstract.html"},{"key":"9339_CR28","doi-asserted-by":"publisher","unstructured":"He, Q., Agu, E., Strong, D.M., Tulu, B.: Recfit: a context-aware system for recommending physical activities. In: Gupta, S.K.S., Banerjee, A. (eds.) Proceedings of the 1st Workshop on Mobile Medical Applications, MMA \u201914, Memphis, Tennessee, USA, November 3-6, 2014, pp. 34\u201339. ACM (2014). https:\/\/doi.org\/10.1145\/2676431.2676439","DOI":"10.1145\/2676431.2676439"},{"key":"9339_CR29","doi-asserted-by":"publisher","unstructured":"Hutson, J.A., Taft, J.G., Barocas, S., Levy, K.: Debiasing desire: Addressing bias and discrimination on intimate platforms. Proc. ACM Hum. Comput. Interact. 2(CSCW), 73:1\u201373:18 (2018). https:\/\/doi.org\/10.1145\/3274342","DOI":"10.1145\/3274342"},{"key":"9339_CR30","unstructured":"Kamishima, T., Akaho, S., Asoh, H., Sakuma, J.: Recommendation independence. In: Conference on Fairness, Accountability and Transparency, FAT 2018, Proceedings of Machine Learning Research, vol.\u00a081, pp. 187\u2013201. PMLR (2018). http:\/\/proceedings.mlr.press\/v81\/kamishima18a.html"},{"issue":"4","key":"9339_CR31","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1007\/s00779-017-1020-6","volume":"21","author":"BA Kamphorst","year":"2017","unstructured":"Kamphorst, B.A.: E-coaching systems - what they are, and what they aren\u2019t. Pers. Ubiquit. Comput. 21(4), 625\u2013632 (2017). https:\/\/doi.org\/10.1007\/s00779-017-1020-6","journal-title":"Pers. Ubiquit. Comput."},{"key":"9339_CR32","doi-asserted-by":"publisher","unstructured":"Khwaja, M., Ferrer, M., Iglesias, J.O., Faisal, A.A., Matic, A.: Aligning daily activities with personality: towards a recommender system for improving wellbeing. In: Bogers, T., Said, A., Brusilovsky, P., Tikk, D. (eds.) Proceedings of the 13th ACM Conference on Recommender Systems, RecSys 2019, Copenhagen, Denmark, September 16-20, 2019, pp. 368\u2013372. ACM (2019). https:\/\/doi.org\/10.1145\/3298689.3347020","DOI":"10.1145\/3298689.3347020"},{"issue":"4","key":"9339_CR33","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MIC.2015.51","volume":"19","author":"MCA Klein","year":"2015","unstructured":"Klein, M.C.A., Manzoor, A.R., Middelweerd, A., Mollee, J.S., te Velde, S.J.: Encouraging physical activity via a personalized mobile system. IEEE Int. Comput. 19(4), 20\u201327 (2015). https:\/\/doi.org\/10.1109\/MIC.2015.51","journal-title":"IEEE Int. Comput."},{"key":"9339_CR34","unstructured":"Klement, W., Wilk, S., Michaowski, W., Matwin, S.: Dealing with severely imbalanced data. In: Proceedings of the PAKDD Conference, p.\u00a014. Citeseer (2009)"},{"issue":"3","key":"9339_CR35","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1207\/s15324796abm3103_2","volume":"31","author":"W Kroeze","year":"2006","unstructured":"Kroeze, W., Werkman, A., Brug, J.: A systematic review of randomized trials on the effectiveness of computer-tailored education on physical activity and dietary behaviors. Ann. Behav. Med. 31(3), 205\u2013223 (2006)","journal-title":"Ann. Behav. Med."},{"issue":"6","key":"9339_CR36","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1111\/ppa.12041","volume":"62","author":"S Landschoot","year":"2013","unstructured":"Landschoot, S., Waegeman, W., Audenaert, K., Haesaert, G., De Baets, B.: Ordinal regression models for predicting deoxynivalenol in winter wheat. Plant. Pathol. 62(6), 1319\u20131329 (2013)","journal-title":"Plant. Pathol."},{"key":"9339_CR37","unstructured":"Lema\u00eetre, G., Nogueira, F., Aridas, C.K.: Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning. J. Mach. Learn. Res. 18(17), 1\u20135 (2017). http:\/\/jmlr.org\/papers\/v18\/16-365"},{"key":"9339_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-021-00271-1","author":"M Marras","year":"2021","unstructured":"Marras, M., Boratto, L., Ramos, G., Fenu, G.: Equality of learning opportunity via individual fairness in personalized recommendations. Int. J. Artif. Intell. Educ. (2021). https:\/\/doi.org\/10.1007\/s40593-021-00271-1","journal-title":"Int. J. Artif. Intell. Educ."},{"key":"9339_CR39","doi-asserted-by":"publisher","unstructured":"Marras, M., Korus, P., Memon, N.D., Fenu, G.: Adversarial optimization for dictionary attacks on speaker verification. In: G.\u00a0Kubin, Z.\u00a0Kacic (eds.) Interspeech 2019, 20th Annual Conference of the International Speech Communication Association, Graz, Austria, 15-19 September 2019, pp. 2913\u20132917. ISCA (2019). https:\/\/doi.org\/10.21437\/Interspeech.2019-2430","DOI":"10.21437\/Interspeech.2019-2430"},{"issue":"1","key":"9339_CR40","doi-asserted-by":"publisher","DOI":"10.2196\/mhealth.5027","volume":"4","author":"CK Martin","year":"2016","unstructured":"Martin, C.K., Gilmore, L.A., Apolzan, J.W., Myers, C.A., Thomas, D.M., Redman, L.M.: Smartloss: a personalized mobile health intervention for weight management and health promotion. JMIR Mhealth Uhealth 4(1), e18 (2016)","journal-title":"JMIR Mhealth Uhealth"},{"key":"9339_CR41","unstructured":"McMurray, J., Adamopoulos, S., Anker, S., Auricchio, A., Bohm, M., Dickstein, K et al.: esc guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The task force for the diagnosis and treatment of acute and chronic heart failure 2012 of the european society of cardiology. developed in collaboration with the heart failure association (hfa) of the esc (1787)"},{"key":"9339_CR42","doi-asserted-by":"publisher","unstructured":"Mehrotra, R., McInerney, J., Bouchard, H., Lalmas, M., Diaz, F.: Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness and satisfaction in recommendation systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, pp. 2243\u20132251. ACM (2018). https:\/\/doi.org\/10.1145\/3269206.3272027","DOI":"10.1145\/3269206.3272027"},{"key":"9339_CR43","doi-asserted-by":"crossref","unstructured":"Nassabi, M.H., op\u00a0den Akker, H., Vollenbroek-Hutten, M.M.R.: An ontology-based recommender system to promote physical activity for pre-frail elderly. In: Butz, A., Koch, M., Schlichter, J.H. (eds.) Mensch and Computer 2014 - Workshopband, 14. Fach\u00fcbergreifende Konferenz f\u00fcr Interaktive und Kooperative Medien - Interaktiv unterwegs - Freir\u00e4ume gestalten, 31. August - 3. September 2014, M\u00fcnchen, Germany, pp. 181\u2013184. De Gruyter Oldenbourg (2014). https:\/\/dl.gi.de\/20.500.12116\/8167","DOI":"10.1524\/9783110344509.181"},{"key":"9339_CR44","doi-asserted-by":"publisher","unstructured":"Patro, G.K., Biswas, A., Ganguly, N., Gummadi, K.P., Chakraborty, A.: Fairrec: Two-sided fairness for personalized recommendations in two-sided platforms. In: WWW \u201920: The Web Conference 2020, pp. 1194\u20131204. ACM \/ IW3C2 (2020). https:\/\/doi.org\/10.1145\/3366423.3380196","DOI":"10.1145\/3366423.3380196"},{"key":"9339_CR45","doi-asserted-by":"publisher","unstructured":"Petsani, D., Konstantinidis, E.I., Bamidis, P.D.: Designing an e-coaching system for older people to increase adherence to exergame-based physical activity. In: Bamidis, P.D., Ziefle, M., Maciaszek, L.A. (eds.) Proceedings of the 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health, ICT4AWE 2018, Funchal, Madeira, Portugal, March 22-23, 2018, pp. 258\u2013263. SciTePress (2018). https:\/\/doi.org\/10.5220\/0006821502580263","DOI":"10.5220\/0006821502580263"},{"key":"9339_CR46","unstructured":"Powers, D.M.: Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation (2011)"},{"key":"9339_CR47","unstructured":"Powers, D.M.: The problem with kappa. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 345\u2013355. Association for Computational Linguistics (2012)"},{"key":"9339_CR48","doi-asserted-by":"crossref","unstructured":"Radlinski, F., Craswell, N.: Comparing the sensitivity of information retrieval metrics. In: Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pp. 667\u2013674. ACM (2010)","DOI":"10.1145\/1835449.1835560"},{"issue":"3","key":"9339_CR49","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00607-016-0489-6","volume":"99","author":"SS Rathore","year":"2017","unstructured":"Rathore, S.S., Kumar, S.: A decision tree logic based recommendation system to select software fault prediction techniques. Computing 99(3), 255\u2013285 (2017)","journal-title":"Computing"},{"key":"9339_CR50","unstructured":"Rennie, J.D.: Ordinal Logistic Regression. MIT (2005)"},{"key":"9339_CR51","unstructured":"Rennie, J.D., Srebro, N.: Loss functions for preference levels: Regression with discrete ordered labels. In: Proceedings of the IJCAI multidisciplinary workshop on advances in preference handling, vol.\u00a01. Citeseer (2005)"},{"key":"9339_CR52","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.patcog.2016.03.012","volume":"57","author":"JA S\u00e1ez","year":"2016","unstructured":"S\u00e1ez, J.A., Krawczyk, B., Wo\u017aniak, M.: Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets. Pattern Recogn. 57, 164\u2013178 (2016)","journal-title":"Pattern Recogn."},{"key":"9339_CR53","doi-asserted-by":"publisher","unstructured":"Santos-Gago, J.M., Sabucedo, L.\u00c1., Gonz\u00e1lez-Maciel, R., Ror\u00eds, V.M.A., Garc\u00eda-Soid\u00e1n, J.L., Wanden-Berghe, C., Sanz-Valero, J.: Towards a personalised recommender platform for sportswomen. In: Rocha, \u00c1., Adeli, H., Reis, L.P., Costanzo, S. (eds.) New Knowledge in Information Systems and Technologies - Volume 1, World Conference on Information Systems and Technologies, WorldCIST 2019, Galicia, Spain, 16-19 April, 2019, Advances in Intelligent Systems and Computing, vol. 930, pp. 504\u2013514. Springer (2019). https:\/\/doi.org\/10.1007\/978-3-030-16181-1_48","DOI":"10.1007\/978-3-030-16181-1_48"},{"key":"9339_CR54","doi-asserted-by":"publisher","unstructured":"Sanz-Cruzado, J., Castells, P.: Contact Recommendations in Social Networks, chap. Chapter 16, pp. 519\u2013569 (2019). https:\/\/doi.org\/10.1142\/9789813275355_0016","DOI":"10.1142\/9789813275355_0016"},{"key":"9339_CR55","doi-asserted-by":"publisher","unstructured":"Singh, A., Joachims, T.: Fairness of exposure in rankings. In: Guo, Y., Farooq, F. (eds.) Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, UK, August 19-23, 2018, pp. 2219\u20132228. ACM (2018). https:\/\/doi.org\/10.1145\/3219819.3220088","DOI":"10.1145\/3219819.3220088"},{"key":"9339_CR56","doi-asserted-by":"publisher","unstructured":"Smyth, B.: Recommender systems: A healthy obsession. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pp. 9790\u20139794. AAAI Press (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33019790","DOI":"10.1609\/aaai.v33i01.33019790"},{"key":"9339_CR57","doi-asserted-by":"publisher","unstructured":"Smyth, B., Cunningham, P.: An analysis of case representations for marathon race prediction and planning. In: Cox, M.T., Funk, P., Begum, S. (eds.) Case-Based Reasoning Research and Development - 26th International Conference, ICCBR 2018, Stockholm, Sweden, July 9-12, 2018, Proceedings, Lecture Notes in Computer Science, vol. 11156, pp. 369\u2013384. Springer (2018). https:\/\/doi.org\/10.1007\/978-3-030-01081-2_25","DOI":"10.1007\/978-3-030-01081-2_25"},{"key":"9339_CR58","doi-asserted-by":"publisher","unstructured":"Smyth, B., Cunningham, P.: Marathon race planning: A case-based reasoning approach. In: Lang, J. (ed.) Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, pp. 5364\u20135368. ijcai.org (2018). https:\/\/doi.org\/10.24963\/ijcai.2018\/754","DOI":"10.24963\/ijcai.2018\/754"},{"key":"9339_CR59","unstructured":"Topal, M., Eyduran, E., Ya\u011fano\u011flu, A., S\u00f6nmaz, A., Keskin, S., et\u00a0al.: Use of ridge and principal component regression analysis methods in multicollinearity. Journal of the Faculty of Agriculture of Atat\u00fcrk University (Turkey) (2010)"},{"key":"9339_CR60","doi-asserted-by":"publisher","unstructured":"Tseng, J.C.C., Lin, B., Lin, Y., Tseng, V.S., Day, M., Wang, S., Lo, K., Yang, Y.: An interactive healthcare system with personalized diet and exercise guideline recommendation. In: Conference on Technologies and Applications of Artificial Intelligence, TAAI 2015, Tainan, Taiwan, November 20-22, 2015, pp. 525\u2013532. IEEE (2015). https:\/\/doi.org\/10.1109\/TAAI.2015.7407106","DOI":"10.1109\/TAAI.2015.7407106"},{"key":"9339_CR61","doi-asserted-by":"publisher","unstructured":"Yang, K., Stoyanovich, J.: Measuring fairness in ranked outputs. In: Proceedings of the 29th International Conference on Scientific and Statistical Database Management, Chicago, IL, USA, June 27-29, 2017, pp. 22:1\u201322:6. ACM (2017). https:\/\/doi.org\/10.1145\/3085504.3085526","DOI":"10.1145\/3085504.3085526"},{"issue":"10","key":"9339_CR62","volume":"19","author":"E Yom-Tov","year":"2017","unstructured":"Yom-Tov, E., Feraru, G., Kozdoba, M., Mannor, S., Tennenholtz, M., Hochberg, I.: Encouraging physical activity in patients with diabetes: intervention using a reinforcement learning system. J. Med. Int. Res. 19(10), e338 (2017)","journal-title":"J. Med. Int. Res."},{"key":"9339_CR63","doi-asserted-by":"publisher","unstructured":"Zehlike, M., Bonchi, F., Castillo, C., Hajian, S., Megahed, M., Baeza-Yates, R.: Fair: A fair top-k ranking algorithm. In: Lim, E., Winslett, M., Sanderson, M., Fu, A.W., Sun, J., Culpepper, J.S., Lo, E., Ho, J.C., Donato, D., Agrawal, R., Zheng, Y., Castillo, C., Sun, A., Tseng, V.S., Li, C. (eds.) Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017, Singapore, November 06\u201310, 2017, pp. 1569\u20131578. ACM (2017). https:\/\/doi.org\/10.1145\/3132847.3132938","DOI":"10.1145\/3132847.3132938"},{"key":"9339_CR64","doi-asserted-by":"publisher","unstructured":"Zehlike, M., Castillo, C.: Reducing disparate exposure in ranking: A learning to rank approach. In: WWW \u201920: The Web Conference 2020, pp. 2849\u20132855. ACM \/ IW3C2 (2020). https:\/\/doi.org\/10.1145\/3366424.3380048","DOI":"10.1145\/3366424.3380048"}],"container-title":["User Modeling and User-Adapted Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-022-09339-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11257-022-09339-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-022-09339-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,26]],"date-time":"2022-11-26T22:21:41Z","timestamp":1669501301000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11257-022-09339-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,5]]},"references-count":64,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["9339"],"URL":"https:\/\/doi.org\/10.1007\/s11257-022-09339-6","relation":{},"ISSN":["0924-1868","1573-1391"],"issn-type":[{"type":"print","value":"0924-1868"},{"type":"electronic","value":"1573-1391"}],"subject":[],"published":{"date-parts":[[2022,8,5]]},"assertion":[{"value":"19 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}