{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T14:56:34Z","timestamp":1782572194042,"version":"3.54.5"},"reference-count":111,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T00:00:00Z","timestamp":1690848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003977","name":"Israel Science Foundation","doi-asserted-by":"publisher","award":["2243\/20"],"award-info":[{"award-number":["2243\/20"]}],"id":[{"id":"10.13039\/501100003977","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":[[2024,4]]},"DOI":"10.1007\/s11257-023-09376-9","type":"journal-article","created":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T06:02:49Z","timestamp":1690869769000},"page":"375-405","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Modeling users\u2019 heterogeneous taste with diversified attentive user profiles"],"prefix":"10.1007","volume":"34","author":[{"given":"Oren","family":"Barkan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Shaked","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonatan","family":"Fuchs","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8219-4512","authenticated-orcid":false,"given":"Noam","family":"Koenigstein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,1]]},"reference":[{"key":"9376_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal, D., Chen, B.-C.: Regression-based latent factor models. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 19\u201328 (2009)","DOI":"10.1145\/1557019.1557029"},{"key":"9376_CR2","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C.: Neighborhood-based collaborative filtering, pp. 29\u201370 (2016)","DOI":"10.1007\/978-3-319-29659-3_2"},{"key":"9376_CR3","volume-title":"Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics","author":"W Albert","year":"2013","unstructured":"Albert, W., Tullis, T.: Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics. Morgan Kaufmann, Burlington, Massachusetts, United States (2013)"},{"key":"9376_CR4","unstructured":"Barkan, O., Armstrong, O., Hertz, A., Caciularu, A., Katz, O., Malkiel, I., Koenigstein, N.: Gam: Explainable visual similarity and classification via gradient activation maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 68\u201377 (2021)"},{"key":"9376_CR5","unstructured":"Barkan, O., Brumer, Y., Koenigstein, N.: Modelling session activity with neural embedding (2016)"},{"key":"9376_CR6","doi-asserted-by":"crossref","unstructured":"Barkan, O., Caciularu, A., Dagan, I.: Within-between lexical relation classification. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3521\u20133527 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.284"},{"key":"9376_CR7","doi-asserted-by":"crossref","unstructured":"Barkan, O., Caciularu, A., Katz, O., Koenigstein, N.: Attentive item2vec: Neural attentive user representations. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020)","DOI":"10.1109\/ICASSP40776.2020.9053071"},{"key":"9376_CR8","doi-asserted-by":"crossref","unstructured":"Barkan, O., Caciularu, A., Rejwan, I., Katz, O., Weill, J., Malkiel, I., Koenigstein, N.: Cold item recommendations via hierarchical item2vec. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 912\u2013917 (2020). IEEE Computer Society","DOI":"10.1109\/ICDM50108.2020.00101"},{"key":"9376_CR9","doi-asserted-by":"crossref","unstructured":"Barkan, O., Caciularu, A., Rejwan, I., Katz, O., Weill, J., Malkiel, I., Koenigstein, N.: Representation learning via variational bayesian networks. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 78\u201388 (2021)","DOI":"10.1145\/3459637.3482363"},{"key":"9376_CR10","doi-asserted-by":"crossref","unstructured":"Barkan, O., Fuchs, Y., Caciularu, A., Koenigstein, N.: Explainable recommendations via attentive multi-persona collaborative filtering. In: Proceedings of the 14th ACM Conference on Recommender Systems, pp. 468\u2013473 (2020)","DOI":"10.1145\/3383313.3412226"},{"key":"9376_CR11","doi-asserted-by":"crossref","unstructured":"Barkan, O., Hauon, E., Caciularu, A., Katz, O., Malkiel, I., Armstrong, O., Koenigstein, N.: Grad-sam: explaining transformers via gradient self-attention maps. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2882\u20132887 (2021)","DOI":"10.1145\/3459637.3482126"},{"key":"9376_CR12","doi-asserted-by":"crossref","unstructured":"Barkan, O., Hirsch, R., Katz, O., Caciularu, A., Koenigstein, N.: Anchor-based collaborative filtering. In: Proceedings of the ACM International Conference on Information & Knowledge Management (CIKM) (2021)","DOI":"10.1145\/3459637.3482056"},{"key":"9376_CR13","doi-asserted-by":"crossref","unstructured":"Barkan, O., Hirsch, R., Katz, O., Caciularu, A., Weill, J., Koenigstein, N.: Cold item integration in deep hybrid recommenders via tunable stochastic gates. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 994\u2013999 (2021). IEEE","DOI":"10.1109\/ICDM51629.2021.00112"},{"key":"9376_CR14","doi-asserted-by":"crossref","unstructured":"Barkan, O., Hirsch, R., Katz, O., Caciularu, A., Weill, Y., Koenigstein, N.: Cold start revisited: a deep hybrid recommender with cold-warm item harmonization. In: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3260\u20133264 (2021). IEEE","DOI":"10.1109\/ICASSP39728.2021.9413384"},{"key":"9376_CR15","doi-asserted-by":"crossref","unstructured":"Barkan, O., Katz, O., Koenigstein, N.: Neural attentive multiview machines. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020)","DOI":"10.1109\/ICASSP40776.2020.9053105"},{"key":"9376_CR16","doi-asserted-by":"crossref","unstructured":"Barkan, O., Koenigstein, N., Yogev, E., Katz, O.: Cb2cf: A neural multiview content-to-collaborative filtering model for completely cold item recommendations. In: Proceedings of the ACM Conference on Recommender Systems (RecSys) (2019)","DOI":"10.1145\/3298689.3347038"},{"key":"9376_CR17","doi-asserted-by":"crossref","unstructured":"Barkan, O., Koenigstein, N.: Item2vec: neural item embedding for collaborative filtering. In: the IEEE International Workshop on Machine Learning for Signal Processing (MLSP) (2016)","DOI":"10.1109\/MLSP.2016.7738886"},{"key":"9376_CR18","doi-asserted-by":"crossref","unstructured":"Barkan, O., Razin, N., Malkiel, I., Katz, O., Caciularu, A., Koenigstein, N.: Scalable attentive sentence pair modeling via distilled sentence embedding. In: Proceedings of the International Conference on Artificial Intelligence (AAAI) (2020)","DOI":"10.1609\/aaai.v34i04.5722"},{"key":"9376_CR19","doi-asserted-by":"crossref","unstructured":"Barkan, O., Rejwan, I., Caciularu, A., Koenigstein, N.: Bayesian hierarchical words representation learning. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) (2020)","DOI":"10.18653\/v1\/2020.acl-main.356"},{"key":"9376_CR20","doi-asserted-by":"crossref","unstructured":"Barkan, O.: Bayesian neural word embedding. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10987"},{"key":"9376_CR21","unstructured":"Bell, R.M., Koren, Y.: Improved neighborhood-based collaborative filtering. In: KDD Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 7\u201314 (2007). Citeseer"},{"key":"9376_CR22","doi-asserted-by":"crossref","unstructured":"Bell, R.M., Koren, Y.: Lessons from the netflix prize challenge. SIGKDD Explor. Newsl., 75\u201379 (2007)","DOI":"10.1145\/1345448.1345465"},{"key":"9376_CR23","doi-asserted-by":"crossref","unstructured":"Bellogin, A., Castells, P., Cantador, I.: Precision-oriented evaluation of recommender systems: an algorithmic comparison. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 333\u2013336 (2011)","DOI":"10.1145\/2043932.2043996"},{"key":"9376_CR24","unstructured":"Bennett, J., Lanning, S., : The netflix prize. In: Proceedings of KDD Cup and Workshop, vol. 2007, p. 35 (2007). New York, NY, USA"},{"key":"9376_CR25","unstructured":"Berg, R.v.d., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)"},{"key":"9376_CR26","unstructured":"Bilgic, M., Mooney, R.J.: Explaining recommendations: satisfaction vs. promotion. In: Beyond Personalization Workshop, IUI, vol. 5, p. 153 (2005)"},{"key":"9376_CR27","unstructured":"Biran, O., Cotton, C.: Explanation and justification in machine learning: A survey. In: IJCAI-17 Workshop on Explainable AI (XAI), vol. 8, pp. 8\u201313 (2017)"},{"key":"9376_CR28","doi-asserted-by":"crossref","unstructured":"Boim, R., Milo, T., Novgorodov, S.: Diversification and refinement in collaborative filtering recommender. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 739\u2013744 (2011)","DOI":"10.1145\/2063576.2063684"},{"key":"9376_CR29","unstructured":"Bradley, K., Smyth, B.: Improving recommendation diversity. In: Proceedings of the Twelfth Irish Conference on Artificial Intelligence and Cognitive Science, Maynooth, Ireland, vol. 85, pp. 141\u2013152 (2001). Citeseer"},{"key":"9376_CR30","unstructured":"Brumer, Y., Shapira, B., Rokach, L., Barkan, O.: Predicting relevance scores for triples from type-like relations using neural embedding-the cabbage triple scorer at wsdm cup 2017. arXiv preprint arXiv:1712.08359 (2017)"},{"key":"9376_CR31","doi-asserted-by":"crossref","unstructured":"Cai, D., He, X., Wu, X., Han, J.: Non-negative matrix factorization on manifold. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 63\u201372 (2008). IEEE","DOI":"10.1109\/ICDM.2008.57"},{"key":"9376_CR32","doi-asserted-by":"crossref","unstructured":"Castells, P., Hurley, N., Vargas, S.: Novelty and diversity in recommender systems, 603\u2013646 (2022)","DOI":"10.1007\/978-1-0716-2197-4_16"},{"key":"9376_CR33","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, H., He, X., Nie, L., Liu, W., Chua, T.-S.: Attentive collaborative filtering: multimedia recommendation with item- and component-level attention. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (2017)","DOI":"10.1145\/3077136.3080797"},{"issue":"2","key":"9376_CR34","first-page":"1","volume":"38","author":"C Chen","year":"2020","unstructured":"Chen, C., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Efficient neural matrix factorization without sampling for recommendation. ACM Trans. Inf. Syst. (TOIS) 38(2), 1\u201328 (2020)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"9376_CR35","unstructured":"Choi, D., Shallue, C.J., Nado, Z., Lee, J., Maddison, C.J., Dahl, G.E.: On empirical comparisons of optimizers for deep learning. arXiv preprint arXiv:1910.05446 (2019)"},{"key":"9376_CR36","unstructured":"Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)"},{"issue":"5","key":"9376_CR37","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1007\/s11257-008-9051-3","volume":"18","author":"H Cramer","year":"2008","unstructured":"Cramer, H., Evers, V., Ramlal, S., Van Someren, M., Rutledge, L., Stash, N., Aroyo, L., Wielinga, B.: The effects of transparency on trust in and acceptance of a content-based art recommender. User Model. User-Adapt. Interact. 18(5), 455\u2013496 (2008)","journal-title":"User Model. User-Adapt. Interact."},{"key":"9376_CR38","unstructured":"Dror, G., Koenigstein, N., Koren, Y., Weimer, M.: The yahoo! music dataset and kdd-cup\u201911. In: Proceedings of KDD Cup (2012)"},{"key":"9376_CR39","unstructured":"Dror, G., Koenigstein, N., Koren, Y., Weimer, M.: The yahoo! music dataset and KDD-cup\u201911. Proc. KDD Cup 2011, 3\u201318 (2012). (PMLR)"},{"key":"9376_CR40","doi-asserted-by":"crossref","unstructured":"Du, X., He, X., Yuan, F., Tang, J., Qin, Z., Chua, T.-S.: Modeling embedding dimension correlations via convolutional neural collaborative filtering. ACM Trans. Inf. Syst. 37(4) (2019)","DOI":"10.1145\/3357154"},{"key":"9376_CR41","doi-asserted-by":"crossref","unstructured":"Ebesu, T., Shen, B., Fang, Y.: Collaborative memory network for recommendation systems. In: The International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (2018)","DOI":"10.1145\/3209978.3209991"},{"key":"9376_CR42","doi-asserted-by":"crossref","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 (2017)","DOI":"10.1145\/3079628.3079699"},{"issue":"02","key":"9376_CR43","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1142\/S0218001407005417","volume":"21","author":"A Felfernig","year":"2007","unstructured":"Felfernig, A., Teppan, E., Gula, B.: Knowledge-based recommender technologies for marketing and sales. Int. J. Pattern Recognit. Artif. Intell. 21(02), 333\u2013354 (2007)","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"9376_CR44","doi-asserted-by":"publisher","first-page":"34746","DOI":"10.1109\/ACCESS.2023.3263931","volume":"11","author":"K Gaiger","year":"2023","unstructured":"Gaiger, K., Barkan, O., Tsipory-Samuel, S., Koenigstein, N.: Not all memories created equal: Dynamic user representations for collaborative filtering. IEEE Access 11, 34746\u201334763 (2023)","journal-title":"IEEE Access"},{"key":"9376_CR45","unstructured":"Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249\u2013256 (2010). JMLR Workshop and Conference Proceedings"},{"key":"9376_CR46","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.knosys.2017.03.023","volume":"125","author":"A Gogna","year":"2017","unstructured":"Gogna, A., Majumdar, A.: Balancing accuracy and diversity in recommendations using matrix completion framework. Knowl. Based Syst. 125, 83\u201395 (2017)","journal-title":"Knowl. Based Syst."},{"key":"9376_CR47","doi-asserted-by":"crossref","unstructured":"Gunawardana, A., Shani, G.: Evaluating recommender systems. In: Recommender Systems Handbook, pp. 265\u2013308 (2015)","DOI":"10.1007\/978-1-4899-7637-6_8"},{"key":"9376_CR48","doi-asserted-by":"crossref","unstructured":"Guy, I., Ronen, I., Wilcox, E.: Do you know? recommending people to invite into your social network. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, pp. 77\u201386 (2009)","DOI":"10.1145\/1502650.1502664"},{"key":"9376_CR49","doi-asserted-by":"crossref","unstructured":"Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5(4) (2015)","DOI":"10.1145\/2827872"},{"key":"9376_CR50","doi-asserted-by":"crossref","unstructured":"He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the International Conference on World Wide Web (WWW) (2017)","DOI":"10.1145\/3038912.3052569"},{"key":"9376_CR51","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.: Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the International Conference on World Wide Web (WWW) (2016)","DOI":"10.1145\/2872427.2883037"},{"key":"9376_CR52","doi-asserted-by":"crossref","unstructured":"He, X., Zhang, H., Kan, M.-Y., Chua, T.-S.: Fast matrix factorization for online recommendation with implicit feedback. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (2016)","DOI":"10.1145\/2911451.2911489"},{"key":"9376_CR53","unstructured":"Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415 (2016)"},{"key":"9376_CR54","doi-asserted-by":"crossref","unstructured":"Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work, pp. 241\u2013250 (2000)","DOI":"10.1145\/358916.358995"},{"issue":"4","key":"9376_CR55","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1023\/A:1020443909834","volume":"5","author":"J Herlocker","year":"2002","unstructured":"Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(4), 287\u2013310 (2002)","journal-title":"Inf. Retr."},{"key":"9376_CR56","doi-asserted-by":"crossref","unstructured":"Holzinger, A., Saranti, A., Molnar, C., Biecek, P., Samek, W.: Explainable ai methods-a brief overview. In: International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, pp. 13\u201338 (2022). Springer","DOI":"10.1007\/978-3-031-04083-2_2"},{"key":"9376_CR57","doi-asserted-by":"crossref","unstructured":"Hurley, N.J.: Personalised ranking with diversity. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 379\u2013382 (2013)","DOI":"10.1145\/2507157.2507226"},{"key":"9376_CR58","doi-asserted-by":"crossref","unstructured":"Jannach, D., Zanker, M.: Value and impact of recommender systems, pp. 519\u2013546 (2022)","DOI":"10.1007\/978-1-0716-2197-4_14"},{"key":"9376_CR59","doi-asserted-by":"crossref","unstructured":"Kabbur, S., Ning, X., Karypis, G.: Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 659\u2013667 (2013)","DOI":"10.1145\/2487575.2487589"},{"key":"9376_CR60","doi-asserted-by":"crossref","unstructured":"Katz, O., Barkan, O., Koenigstein, N., Zabari, N.: Learning to ride a buy-cycle: A hyper-convolutional model for next basket repurchase recommendation. In: Proceedings of the 16th ACM Conference on Recommender Systems, pp. 316\u2013326 (2022)","DOI":"10.1145\/3523227.3546763"},{"key":"9376_CR61","doi-asserted-by":"crossref","unstructured":"Kaya, M., Bridge, D.: A comparison of calibrated and intent-aware recommendations. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 151\u2013159 (2019)","DOI":"10.1145\/3298689.3347045"},{"issue":"3","key":"9376_CR62","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s11257-019-09235-6","volume":"29","author":"M Kaya","year":"2019","unstructured":"Kaya, M., Bridge, D.: Subprofile-aware diversification of recommendations. User Model. User-Adapt. Interact. 29(3), 661\u2013700 (2019)","journal-title":"User Model. User-Adapt. Interact."},{"key":"9376_CR63","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)"},{"issue":"4","key":"9376_CR64","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s11257-011-9118-4","volume":"22","author":"BP Knijnenburg","year":"2012","unstructured":"Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Model. user-Adapt. Interact. 22(4), 441\u2013504 (2012)","journal-title":"User Model. user-Adapt. Interact."},{"key":"9376_CR65","doi-asserted-by":"crossref","unstructured":"Koenigstein, N.: Rethinking collaborative filtering: a practical perspective on state-of-the-art research based on real world insights. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 336\u2013337 (2017)","DOI":"10.1145\/3109859.3109919"},{"key":"9376_CR66","doi-asserted-by":"crossref","unstructured":"Kohavi, R., Deng, A., Frasca, B., Walker, T., Xu, Y., Pohlmann, N.: Online controlled experiments at large scale. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1168\u20131176 (2013)","DOI":"10.1145\/2487575.2488217"},{"key":"9376_CR67","doi-asserted-by":"crossref","unstructured":"Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426\u2013434 (2008)","DOI":"10.1145\/1401890.1401944"},{"issue":"8","key":"9376_CR68","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30\u201337 (2009)","journal-title":"Computer"},{"key":"9376_CR69","doi-asserted-by":"crossref","unstructured":"Kunaver, M., Porl, T.: Diversity in recommender systems a survey. Know.-Based Syst., 154\u2013162 (2017)","DOI":"10.1016\/j.knosys.2017.02.009"},{"key":"9376_CR70","doi-asserted-by":"crossref","unstructured":"Li, X., She, J.: Collaborative variational autoencoder for recommender systems. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2017)","DOI":"10.1145\/3097983.3098077"},{"key":"9376_CR71","doi-asserted-by":"crossref","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)","DOI":"10.1145\/3372923.3404793"},{"key":"9376_CR72","doi-asserted-by":"crossref","unstructured":"Malkiel, I., Ginzburg, D., Barkan, O., Caciularu, A., Weill, J., Koenigstein, N.: Interpreting bert-based text similarity via activation and saliency maps. In: Proceedings of the ACM Web Conference 2022, pp. 3259\u20133268 (2022)","DOI":"10.1145\/3485447.3512045"},{"issue":"3","key":"9376_CR73","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.jretai.2020.01.001","volume":"96","author":"A Marchand","year":"2020","unstructured":"Marchand, A., Marx, P.: Automated product recommendations with preference-based explanations. J. Retail. 96(3), 328\u2013343 (2020)","journal-title":"J. Retail."},{"key":"9376_CR74","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 (2006)","DOI":"10.1145\/1125451.1125659"},{"key":"9376_CR75","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS) (2013)"},{"key":"9376_CR76","doi-asserted-by":"crossref","unstructured":"Nguyen, T.T., Kluver, D., Wang, T.-Y., Hui, P.-M., Ekstrand, M.D., Willemsen, M.C., Riedl, J.: Rating support interfaces to improve user experience and recommender accuracy. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 149\u2013156 (2013)","DOI":"10.1145\/2507157.2507188"},{"key":"9376_CR77","doi-asserted-by":"crossref","unstructured":"Niu, W., Caverlee, J., Lu, H.: Neural personalized ranking for image recommendation. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 423\u2013431 (2018)","DOI":"10.1145\/3159652.3159728"},{"issue":"3","key":"9376_CR78","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s10618-011-0215-0","volume":"24","author":"A Papadimitriou","year":"2012","unstructured":"Papadimitriou, A., Symeonidis, P., Manolopoulos, Y.: A generalized taxonomy of explanations styles for traditional and social recommender systems. Data Min. Knowl. Discov. 24(3), 555\u2013583 (2012)","journal-title":"Data Min. Knowl. Discov."},{"issue":"2","key":"9376_CR79","doi-asserted-by":"publisher","first-page":"51","DOI":"10.2753\/JEC1086-4415180202","volume":"18","author":"S-H Park","year":"2013","unstructured":"Park, S.-H., Han, S.P.: From accuracy to diversity in product recommendations: relationship between diversity and customer retention. Int. J. Electr. Commer. 18(2), 51\u201372 (2013)","journal-title":"Int. J. Electr. Commer."},{"key":"9376_CR80","unstructured":"Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) (2009)"},{"key":"9376_CR81","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Lacerda, A., Veloso, A., Ziviani, N.: Pareto-efficient hybridization for multi-objective recommender systems. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 19\u201326 (2012)","DOI":"10.1145\/2365952.2365962"},{"issue":"4","key":"9376_CR82","first-page":"1","volume":"5","author":"MT Ribeiro","year":"2014","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. (TIST) 5(4), 1\u201320 (2014)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"9376_CR83","doi-asserted-by":"crossref","unstructured":"Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook, 1\u201335 (2011)","DOI":"10.1007\/978-0-387-85820-3_1"},{"key":"9376_CR84","doi-asserted-by":"crossref","unstructured":"Santos, R.L., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: Proceedings of the 19th International Conference on World Wide Web, pp. 881\u2013890 (2010)","DOI":"10.1145\/1772690.1772780"},{"key":"9376_CR85","doi-asserted-by":"crossref","unstructured":"Sar Shalom, O., Koenigstein, N., Paquet, U., Vanchinathan, H.P.: Beyond collaborative filtering: the list recommendation problem. In: Proceedings of the 25th International Conference on World Wide Web, pp. 63\u201372 (2016)","DOI":"10.1145\/2872427.2883057"},{"key":"9376_CR86","doi-asserted-by":"publisher","unstructured":"Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. WWW \u201901, pp. 285\u2013295. Association for Computing Machinery, New York, NY, USA (2001). https:\/\/doi.org\/10.1145\/371920.372071","DOI":"10.1145\/371920.372071"},{"key":"9376_CR87","doi-asserted-by":"crossref","unstructured":"Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web, pp. 111\u2013112 (2015)","DOI":"10.1145\/2740908.2742726"},{"key":"9376_CR88","doi-asserted-by":"crossref","unstructured":"Steck, H.: Calibrated recommendations. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 154\u2013162 (2018)","DOI":"10.1145\/3240323.3240372"},{"key":"9376_CR89","doi-asserted-by":"crossref","unstructured":"Steck, H.: Item popularity and recommendation accuracy. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 125\u2013132 (2011)","DOI":"10.1145\/2043932.2043957"},{"key":"9376_CR90","doi-asserted-by":"crossref","unstructured":"Strub, F., Gaudel, R., Mary, J.: Hybrid recommender system based on autoencoders. In: Proceedings of the Workshop on Deep Learning for Recommender Systems (DLRS) (2016)","DOI":"10.1145\/2988450.2988456"},{"key":"9376_CR91","doi-asserted-by":"crossref","unstructured":"Sullivan, E., Bountouridis, D., Harambam, J., Najafian, S., Loecherbach, F., Makhortykh, M., Kelen, D., Wilkinson, D., Graus, D., Tintarev, N.: Reading news with a purpose: explaining user profiles for self-actualization. In: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, pp. 241\u2013245 (2019)","DOI":"10.1145\/3314183.3323456"},{"key":"9376_CR92","unstructured":"Swearingen, K., Sinha, R.: Beyond algorithms: An HCI perspective on recommender systems. In: ACM SIGIR 2001 Workshop on Recommender Systems, vol. 13, pp. 1\u201311 (2001). Citeseer"},{"key":"9376_CR93","doi-asserted-by":"crossref","unstructured":"Tintarev, N., Masthoff, J.: Beyond explaining single item recommendations. Recommender Systems Handbook, 711\u2013756 (2022)","DOI":"10.1007\/978-1-0716-2197-4_19"},{"key":"9376_CR94","doi-asserted-by":"crossref","unstructured":"Tintarev, N., Masthoff, J.: Explaining recommendations: design and evaluation, pp. 353\u2013382 (2015)","DOI":"10.1007\/978-1-4899-7637-6_10"},{"key":"9376_CR95","doi-asserted-by":"crossref","unstructured":"Tintarev, N.: Explaining recommendations. In: International Conference on User Modeling, pp. 470\u2013474 (2007). Springer","DOI":"10.1007\/978-3-540-73078-1_67"},{"key":"9376_CR96","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9(11) (2008)"},{"key":"9376_CR97","unstructured":"Vargas, S., Castells, P.: Exploiting the diversity of user preferences for recommendation. In: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval, pp. 129\u2013136 (2013). Citeseer"},{"key":"9376_CR98","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"9376_CR99","doi-asserted-by":"crossref","unstructured":"Wang, J., de Vries, A.P., Reinders, M.J.T.: Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (2006)","DOI":"10.1145\/1148170.1148257"},{"key":"9376_CR100","doi-asserted-by":"crossref","unstructured":"Wu, Y., DuBois, C., Zheng, A.X., Ester, M.: Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ACM International Conference on Web Search and Data Mining (WSDM) (2016)","DOI":"10.1145\/2835776.2835837"},{"key":"9376_CR101","doi-asserted-by":"crossref","unstructured":"Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., Zhu, J.: Explainable AI: a brief survey on history, research areas, approaches and challenges. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp. 563\u2013574 (2019). Springer","DOI":"10.1007\/978-3-030-32236-6_51"},{"key":"9376_CR102","doi-asserted-by":"crossref","unstructured":"Xue, F., He, X., Wang, X., Xu, J., Liu, K., Hong, R.: Deep item-based collaborative filtering for top-n recommendation. ACM Trans. Inf. Syst. (TOIS) 37(3) (2019)","DOI":"10.1145\/3314578"},{"key":"9376_CR103","doi-asserted-by":"crossref","unstructured":"Yuan, F., Yao, L., Benatallah, B.: Exploring missing interactions: a convolutional generative adversarial network for collaborative filtering. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1773\u20131782 (2020)","DOI":"10.1145\/3340531.3411917"},{"key":"9376_CR104","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ai, Q., Chen, X., Croft, W.B.: Joint representation learning for top-n recommendation with heterogeneous information sources. CIKM \u201917, pp. 1449\u20131458. Association for Computing Machinery, New York, NY, USA (2017)","DOI":"10.1145\/3132847.3132892"},{"key":"9376_CR105","doi-asserted-by":"crossref","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 (2008)","DOI":"10.1145\/1454008.1454030"},{"key":"9376_CR106","doi-asserted-by":"crossref","unstructured":"Zhang, M., Hurley, N.: Novel item recommendation by user profile partitioning. In: 2009 IEEE\/WIC\/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 508\u2013515 (2009). IEEE","DOI":"10.1109\/WI-IAT.2009.85"},{"key":"9376_CR107","doi-asserted-by":"crossref","unstructured":"Zhang, Y.C., S\u00e9aghdha, D.\u00d3., Quercia, D., Jambor, T.: Auralist: introducing serendipity into music recommendation. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 13\u201322 (2012)","DOI":"10.1145\/2124295.2124300"},{"key":"9376_CR108","doi-asserted-by":"crossref","unstructured":"Zheng, L., Lu, C.-T., Jiang, F., Zhang, J., Yu, P.S.: Spectral collaborative filtering. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 311\u2013319 (2018)","DOI":"10.1145\/3240323.3240343"},{"issue":"10","key":"9376_CR109","doi-asserted-by":"publisher","first-page":"4511","DOI":"10.1073\/pnas.1000488107","volume":"107","author":"T Zhou","year":"2010","unstructured":"Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J.R., Zhang, Y.-C.: Solving the apparent diversity-accuracy dilemma of recommender systems. Proc. Natl. Acad. Sci. 107(10), 4511\u20134515 (2010)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"9376_CR110","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Wang, J., Caverlee, J.: Improving top-k recommendation via joint collaborative autoencoders. In: The World Wide Web Conference, pp. 3483\u20133482 (2019)","DOI":"10.1145\/3308558.3313678"},{"key":"9376_CR111","doi-asserted-by":"crossref","unstructured":"Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22\u201332 (2005)","DOI":"10.1145\/1060745.1060754"}],"container-title":["User Modeling and User-Adapted Interaction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-023-09376-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11257-023-09376-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11257-023-09376-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,25]],"date-time":"2024-04-25T09:20:16Z","timestamp":1714036816000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11257-023-09376-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,1]]},"references-count":111,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9376"],"URL":"https:\/\/doi.org\/10.1007\/s11257-023-09376-9","relation":{},"ISSN":["0924-1868","1573-1391"],"issn-type":[{"value":"0924-1868","type":"print"},{"value":"1573-1391","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,1]]},"assertion":[{"value":"21 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}