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Interact. Intell. Syst."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>Recommender systems are ubiquitous and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this article, we study the problem of generating and visualizing personalized explanations for recommender systems that incorporate signals from many different data sources. We use a flexible, extendable probabilistic programming approach and show how we can generate real-time personalized recommendations. We then turn these personalized recommendations into explanations. We perform an extensive user study to evaluate the benefits of explanations for hybrid recommender systems. We conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform. First, we evaluate the performance of the recommendations in terms of perceived accuracy and novelty. Next, we experiment with (1) different explanation styles (e.g., user-based, item-based), (2) manipulating the number of explanation styles presented, and (3) manipulating the presentation format (e.g., textual vs. visual). We also apply a mixed-model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format. Finally, we perform a post analysis that shows different preferences for explanation styles between experienced and novice last.fm users.<\/jats:p>","DOI":"10.1145\/3365843","type":"journal-article","created":{"date-parts":[[2020,11,8]],"date-time":"2020-11-08T11:36:25Z","timestamp":1604835385000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Generating and Understanding Personalized Explanations in Hybrid Recommender Systems"],"prefix":"10.1145","volume":"10","author":[{"given":"Pigi","family":"Kouki","sequence":"first","affiliation":[{"name":"Relational AI, Berkeley, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Schaffer","sequence":"additional","affiliation":[{"name":"Sysco Corporation, Houston, TX, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jay","family":"Pujara","sequence":"additional","affiliation":[{"name":"University of Southern California, Marina del Rey, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"O\u2019Donovan","sequence":"additional","affiliation":[{"name":"UC Santa Barbara, Santa Barbara, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lise","family":"Getoor","sequence":"additional","affiliation":[{"name":"UC Santa Cruz, Santa Cruz, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,11,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"G. 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Conway. 2017. How to recommend?: User trust factors in movie recommender systems. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI\u201917)."},{"key":"e_1_2_1_6_1","volume-title":"Proceedings of the Beyond Personalization Workshop in Conjunction with International Conference on Intelligent User Interfaces (IUI\u201905)","author":"Bilgic M.","unstructured":"M. Bilgic and R. Mooney . 2005. Explaining recommendations: Satisfaction vs. promotion . In Proceedings of the Beyond Personalization Workshop in Conjunction with International Conference on Intelligent User Interfaces (IUI\u201905) . M. Bilgic and R. Mooney. 2005. Explaining recommendations: Satisfaction vs. promotion. 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Bull. 56 2 (1959).  D. Campbell and D. Fiske. 1959. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 56 2 (1959).","DOI":"10.1037\/h0046016"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2018.04.008"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems (RecSys\u201916)","author":"Chang S.","unstructured":"S. Chang , F. Harper , and L. Terveen . 2016. Crowd-based personalized natural language explanations for recommendations . In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys\u201916) . S. Chang, F. Harper, and L. Terveen. 2016. Crowd-based personalized natural language explanations for recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (RecSys\u201916)."},{"key":"e_1_2_1_12_1","volume-title":"Multiplicity control in structural equation modeling. Struct. Eq. 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In Proceedings of the Conference on Computer Supported Cooperative Work (CSCW\u201900)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963772"},{"key":"e_1_2_1_20_1","volume-title":"Mechanical Turk: Now with 40.92% spam. Retrieved on","author":"Ipeirotis P.","year":"2010","unstructured":"P. Ipeirotis . 2010 . Mechanical Turk: Now with 40.92% spam. Retrieved on October 9, 2020 from https:\/\/www.behind-the-enemy-lines.com\/2010\/12\/mechanical-turk-now-with-4092-spam.html. P. Ipeirotis. 2010. Mechanical Turk: Now with 40.92% spam. Retrieved on October 9, 2020 from https:\/\/www.behind-the-enemy-lines.com\/2010\/12\/mechanical-turk-now-with-4092-spam.html."},{"key":"e_1_2_1_21_1","volume-title":"Proceedings of the 10th ACM Conference on Recommender Systems (RecSys\u201916)","author":"Juan Y.","unstructured":"Y. Juan , Y. Zhuang , W. Chin , and C. Lin . 2016. Field-aware factorization machines for CTR prediction . 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In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI\u201919)."},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2014.2346248"},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the 2018 World Wide Web Conference (WWW\u201918)","author":"Lu Y.","unstructured":"Y. Lu , R. Dong , and B. Smyth . 2018. Coevolutionary recommendation model: Mutual learning between ratings and reviews . In Proceedings of the 2018 World Wide Web Conference (WWW\u201918) . Y. Lu, R. Dong, and B. Smyth. 2018. Coevolutionary recommendation model: Mutual learning between ratings and reviews. In Proceedings of the 2018 World Wide Web Conference (WWW\u201918)."},{"key":"e_1_2_1_28_1","volume-title":"Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI\u201919)","author":"Millecamp M.","unstructured":"M. Millecamp , N. N. Htun , C. Conati , and K. Verbert . 2019. 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In Proceedings of the Annual Conference of the Florida Artificial Intelligence Research Society (FLAIRS\u201916)."},{"key":"e_1_2_1_31_1","doi-asserted-by":"crossref","unstructured":"X. Ning C. Desrosiers and G. Karypis. 2015. A comprehensive survey of neighborhood based recommendation methods. In Recommender Systems Handbook (2nd ed.). Springer New York NY.  X. Ning C. Desrosiers and G. Karypis. 2015. A comprehensive survey of neighborhood based recommendation methods. In Recommender Systems Handbook (2nd ed.). Springer New York NY.","DOI":"10.1007\/978-1-4899-7637-6_2"},{"key":"e_1_2_1_32_1","doi-asserted-by":"crossref","unstructured":"I. Nunes and D. Jannach. 2017. A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction (UMUAI\u201917) 27 (2017) 393--444.  I. Nunes and D. Jannach. 2017. A systematic review and taxonomy of explanations in decision support and recommender systems. 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S. Oramas L. Espinosa-Anke M. Sordo H. Saggion and X. Serra. 2016. Information extraction for knowledge base construction in the music domain. Data Knowl. Eng. 106 C (2016).","DOI":"10.1016\/j.datak.2016.06.001"},{"key":"e_1_2_1_35_1","doi-asserted-by":"crossref","unstructured":"A. Papadimitriou P. Symeonidis and Y. Manolopoulos. 2012. A generalized taxonomy of explanations styles for traditional and social recommender systems. Data Min. Knowl. Discov. 24 3 (2012).  A. Papadimitriou P. Symeonidis and Y. Manolopoulos. 2012. A generalized taxonomy of explanations styles for traditional and social recommender systems. Data Min. Knowl. Discov. 24 3 (2012).","DOI":"10.1007\/s10618-011-0215-0"},{"key":"e_1_2_1_36_1","volume-title":"Proceedings of the 19th International Conference on Intelligent User Interfaces (IUI\u201914)","author":"Parra D.","unstructured":"D. Parra , P. Brusilovsky , and C. Trattner . 2014. See what you want to see: Visual user-driven approach for hybrid recommendation . In Proceedings of the 19th International Conference on Intelligent User Interfaces (IUI\u201914) . D. Parra, P. Brusilovsky, and C. Trattner. 2014. See what you want to see: Visual user-driven approach for hybrid recommendation. In Proceedings of the 19th International Conference on Intelligent User Interfaces (IUI\u201914)."},{"key":"e_1_2_1_37_1","volume-title":"Proceedings of the 23rd International Conference on Intelligent User Interfaces (IUI\u201918)","author":"Sato M.","unstructured":"M. Sato , B. Ahsan , K. Nagatani , T. Sonoda , Q. Zhang , and T. Ohkuma . 2018. Explaining recommendations using contexts . In Proceedings of the 23rd International Conference on Intelligent User Interfaces (IUI\u201918) . M. Sato, B. Ahsan, K. Nagatani, T. Sonoda, Q. Zhang, and T. Ohkuma. 2018. Explaining recommendations using contexts. In Proceedings of the 23rd International Conference on Intelligent User Interfaces (IUI\u201918)."},{"key":"e_1_2_1_38_1","volume-title":"Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP\u201918)","author":"Schaffer J.","unstructured":"J. Schaffer , J. O\u2019Donovan , and T. H\u00f6llerer . 2018. Easy to please: Separating user experience from choice satisfaction . In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP\u201918) . J. Schaffer, J. O\u2019Donovan, and T. H\u00f6llerer. 2018. Easy to please: Separating user experience from choice satisfaction. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (UMAP\u201918)."},{"key":"e_1_2_1_39_1","volume-title":"Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics (ACL\u201915)","author":"Sridhar D.","unstructured":"D. Sridhar , J. Foulds , M. Walker , B. Huang , and L. Getoor . 2015. Joint models of disagreement and stance in online debate . In Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics (ACL\u201915) . D. Sridhar, J. Foulds, M. Walker, B. Huang, and L. Getoor. 2015. Joint models of disagreement and stance in online debate. In Proceedings of the 53th Annual Meeting of the Association for Computational Linguistics (ACL\u201915)."},{"key":"e_1_2_1_40_1","volume-title":"Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys\u201909)","author":"Symeonidis P.","unstructured":"P. Symeonidis , A. Nanopoulos , and Y. Manolopoulos . 2009. MoviExplain: A recommender system with explanations . In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys\u201909) . P. Symeonidis, A. Nanopoulos, and Y. Manolopoulos. 2009. MoviExplain: A recommender system with explanations. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys\u201909)."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5116\/ijme.4dfb.8dfd"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-011-9117-5"},{"key":"e_1_2_1_43_1","unstructured":"N. Tintarev and J. Masthoff. 2015. Designing and evaluating explanations for recommender systems. In Recommender Systems Handbook (2nd ed.). Springer New York NY.  N. Tintarev and J. Masthoff. 2015. Designing and evaluating explanations for recommender systems. In Recommender Systems Handbook (2nd ed.). Springer New York NY."},{"key":"e_1_2_1_44_1","doi-asserted-by":"crossref","unstructured":"M. Tkalcic and L. Chen. 2015. Personality and Recommender Systems. Recommender Systems Handbook Second Edition Springer US.  M. Tkalcic and L. Chen. 2015. Personality and Recommender Systems. 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In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI\u201919)."},{"key":"e_1_2_1_48_1","volume-title":"Proceedings of the 11th ACM Conference on Recommender Systems (RecSys\u201919)","author":"Tsukuda K.","unstructured":"K. Tsukuda and M. Goto . 2019. DualDiv: Diversifying items and explanation styles in explainable hybrid recommendation . In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys\u201919) . K. Tsukuda and M. Goto. 2019. DualDiv: Diversifying items and explanation styles in explainable hybrid recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems (RecSys\u201919)."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.2307\/3001913"},{"key":"e_1_2_1_50_1","doi-asserted-by":"crossref","unstructured":"J. Ullman and P. Bentler. 2003. Structural Equation Modeling. Wiley Online Library.  J. Ullman and P. Bentler. 2003. Structural Equation Modeling. 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In Proceedings of the 14th International Conference on Intelligent User Interfaces (IUI\u201909) . J. Vig, S. Sen, and J. Riedl. 2009. Tagsplanations: Explaining recommendations using tags. In Proceedings of the 14th International Conference on Intelligent User Interfaces (IUI\u201909)."},{"key":"e_1_2_1_53_1","volume-title":"Proceedings of the 28th International Joint Conference on Artificial Intelligence. 6050--6056","author":"Zhang Y.","unstructured":"Y. Zhang and A. Ramesh . 2019. Learning interpretable relational structures of hinge-loss Markov random fields . In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 6050--6056 . Y. Zhang and A. Ramesh. 2019. Learning interpretable relational structures of hinge-loss Markov random fields. 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