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“Knowing me, knowing you”: personalized explanations for a music recommender system

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Abstract

Due to the prominent role of recommender systems in our daily lives, it is increasingly important to inform users why certain items are recommended and personalize these explanations to the user. In this study, we explored how explanations in a music recommender system should be designed to fit the preference of different personal characteristics. More specifically, we investigated three personal characteristics that influence the perception of explanations in music recommender system interfaces: need for cognition, musical sophistication, and openness. For each of these personal characteristics, we designed explanations for users with lower and higher levels of the personal characteristic. Afterward, we conducted for each personal characteristic a within-subject user study in which we compared the two explanations. Based on the results of these user studies, we provide design suggestions to adapt explanations to different levels of these three personal characteristics. In general, we suggest providing explanations up-front for all recommendations at once. For users low in need for cognition, displaying these explanations must be optional. To support users with low musical sophistication, we suggest providing brief explanations that do not require domain knowledge. For users with low openness, we suggest providing explanations with a lower number of explanation elements.

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Notes

  1. https://www.gold.ac.uk/music-mind-brain/gold-msi/February2021.

  2. The exact phrasing for the second situation: Please create a playlist of 8 songs to which you would listen during a relaxing activity.

  3. https://www.mturk.com/.

  4. https://developer.spotify.com/documentation/web-api/reference/reference-index.

References

  • Alba, J.W., Hutchinson, J.W.: Dimensions of consumer expertise. J. Consum. Res. 13(4), 411–454 (1987)

    Article  Google Scholar 

  • Anderson, I., Gil, S., Gibson, C., Wolf, S., Shapiro, W., Semerci, O., Greenberg, D.M.: “just the way you area”: linking music listening on spotify and personality. Soc. Psychol. Personal. Sci. 1948550620923228 (2020)

  • Asendorpf, J.B., Neyer, F.J.: Psychologie der persönlichkeit. Springer, New York (2012)

    Book  Google Scholar 

  • Aykin, N.M., Aykin, T.: Individual differences in human–computer interaction. Comput. Ind. Eng. 20(3), 373–379 (1991)

    Article  Google Scholar 

  • Barbaranelli, C., Caprara, G.V.: Studies of the big five questionnaire. In: Big Five Assessment pp. 109–124 (2002)

  • Benet-Martinez, V., John, O.P.: Los cinco grandes across cultures and ethnic groups: multitrait-multimethod analyses of the big five in Spanish and English. J. Pers. Soc. Psychol. 75(3), 729 (1998)

    Article  Google Scholar 

  • Bettman, J.R., Luce, M.F., Payne, J.W.: Constructive consumer choice processes. J. Consum. Res. 25(3), 187–217 (1998)

    Article  Google Scholar 

  • Boy, J., Rensink, R.A., Bertini, E., Fekete, J.D.: A principled way of assessing visualization literacy. IEEE Trans. Vis. Comput. Gr. 20(12), 1963–1972 (2014)

    Article  Google Scholar 

  • Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006)

    Article  Google Scholar 

  • Burnett, M.: Explaining AI: fairly? well? In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp. 1–2 (2020)

  • Cacioppo, J.T., Petty, R.E., Feng Kao, C.: The efficient assessment of need for cognition. J. Pers. Assess. 48(3), 306–307 (1984)

    Article  Google Scholar 

  • Cattell, R.B.: The description of personality: principles and findings in a factor analysis. Am. J. Psychol. 58(1), 69–90 (1945)

    Article  Google Scholar 

  • Chang, S., Harper, F.M., Terveen, L.G.: Crowd-based personalized natural language explanations for recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 175–182 (2016)

  • Chen, L., Wu, W., He, L.: How personality influences users’ needs for recommendation diversity? In: CHI’13 Extended Abstracts on Human Factors in Computing Systems, pp. 829–834 (2013)

  • Chen, X., Chen, H., Xu, H., Zhang, Y., Cao, Y., Qin, Z., Zha, H.: Personalized fashion recommendation with visual explanations based on multimodal attention network: towards visually explainable recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 765–774 (2019)

  • Chernev, A.: When more is less and less is more: the role of ideal point availability and assortment in consumer choice. J. Consum. Res. 30(2), 170–183 (2003)

    Article  Google Scholar 

  • Conati, C., Carenini, G., Hoque, E., Steichen, B., Toker, D.: Evaluating the impact of user characteristics and different layouts on an interactive visualization for decision making. In: Computer Graphics Forum, vol. 33, pp. 371–380. Wiley (2014)

  • Conati, C., Barral, O., Putnam, V., Rieger, L.: Toward personalized Xai: a case study in intelligent tutoring systems. Artif. Intell. J. (2021)

  • Coutinho, S., Wiemer-Hastings, K., Skowronski, J.J., Britt, M.A.: Metacognition, need for cognition and use of explanations during ongoing learning and problem solving. Learn. Individ. Differ. 15(4), 321–337 (2005)

    Article  Google Scholar 

  • Ekstrom, R.B., Dermen, D., Harman, H.H.: Manual for Kit of Factor-Referenced Cognitive Tests, vol. 102. Educational Testing Service, Princeton (1976)

    Google Scholar 

  • Fernández-Tobías, I., Braunhofer, M., Elahi, M., Ricci, F., Cantador, I.: Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model. User-Adap. Int. 26(2–3), 221–255 (2016)

    Article  Google Scholar 

  • Ferwerda, B., Schedl, M.: Personality-based user modeling for music recommender systems. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 254–257. Springer (2016)

  • Ferwerda, B., Graus, M.: Predicting musical sophistication from music listening behaviors: a preliminary study. arXiv preprint arXiv:1808.07314 (2018)

  • Goker, M., Thompson, C.: The adaptive place advisor: A conversational recommendation system. In: Proceedings of the 8th German Workshop on Case Based Reasoning, pp. 187–198. Citeseer (2000)

  • Goldberg, L.R.: An alternative“ description of personality’: the big-five factor structure. J. Pers. Soc. Psychol. 59(6), 1216 (1990)

  • Gosling, S.D., Rentfrow, P.J., Swann, W.B., Jr.: A very brief measure of the big-five personality domains. J. Res. Pers. 37(6), 504–528 (2003)

    Article  Google Scholar 

  • Graus, M., Ferwerda, B.: 1 Theory-grounded user modeling for personalized HCI. Person. Hum. Comput. Interact. (2019)

  • Greb, F., Schlotz, W., Steffens, J.: Personal and situational influences on the functions of music listening. Psychol. Music 46(6), 763–794 (2018)

    Article  Google Scholar 

  • Gunning, D.: Explainable artificial intelligence (Xai), vol. 2(2). Defense Advanced Research Projects Agency (DARPA), nd Web (2017)

  • Hara, K., Adams, A., Milland, K., Savage, S., Callison-Burch, C., Bigham, J.P.: A data-driven analysis of workers’ earnings on amazon mechanical turk. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2018)

  • 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. 241–250. ACM (2000)

  • Hu, R., Pu, P.: Using personality information in collaborative filtering for new users. Recomm. Syst. Soc. Web 17,(2010)

  • Jannach, D., Jugovac, M., Nunes, I.: 5 Explanations and user control in recommender systems. In: Personalized Human–Computer Interaction, p. 32 (2019)

  • Jin, Y., Tintarev, N., Verbert, K.: Effects of individual traits on diversity-aware music recommender user interfaces. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 291–299 (2018a)

  • Jin, Y., Tintarev, N., Verbert, K.: Effects of personal characteristics on music recommender systems with different levels of controllability. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 13–21 (2018b)

  • Jin, Y., Tintarev, N., Htun, N.N., Verbert, K.: Effects of personal characteristics in control-oriented user interfaces for music recommender systems. User Model. User-Adap. Int. 30(2), 199–249 (2020)

    Article  Google Scholar 

  • John, O.P., Donahue, E.M., Kentle, R.L.: The big five inventory-versions 4a and 54 (1991)

  • Johnson, J.A.: Web-based personality assessment. In: 71st Annual Meeting of the Eastern Psychological Association, Baltimore, MD (2000)

  • Jugovac, M., Jannach, D.: Interacting with recommenders-overview and research directions. ACM Trans. Interact. Intell. Syst. 7(3), 1–46 (2017)

    Article  Google Scholar 

  • Jugovac, M., Nunes, I., Jannach, D.: Investigating the decision-making behavior of maximizers and satisficers in the presence of recommendations. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 279–283 (2018)

  • Kamis, A., Davern, M.J.: Personalizing to product category knowledge: exploring the mediating effect of shopping tools on decision confidence. In: Proceedings of the 37th Annual Hawaii International Conference on System Sciences, p. 10. IEEE (2004)

  • Knijnenburg, B.P., Reijmer, N.J., Willemsen, M.C.: Each to his own: how different users call for different interaction methods in recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 141–148 (2011)

  • Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Model. User-Adap. Int. 22(4–5), 441–504 (2012)

    Article  Google Scholar 

  • Kouki, P., Schaffer, J., Pujara, J., O’Donovan, J., Getoor, L.: Personalized explanations for hybrid recommender systems. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 379–390 (2019)

  • Kramer, T.: The effect of measurement task transparency on preference construction and evaluations of personalized recommendations. J. Mark. Res. 44(2), 224–233 (2007)

    Article  MathSciNet  Google Scholar 

  • Kunkel, J., Donkers, T., Michael, L., Barbu, C.M., Ziegler, J.: Let me explain: impact of personal and impersonal explanations on trust in recommender systems. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–12 (2019)

  • Lallé, S., Conati, C., Carenini, G.: Impact of individual differences on user experience with a visualization interface for public engagement. In: Proceedings of UMAP ’17, pp. 247–252. ACM (2017)

  • Lu, Y., Dong, R., Smyth, B.: Why i like it: multi-task learning for recommendation and explanation. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 4–12 (2018)

  • Mahmood, T., Ricci, F., Venturini, A., Höpken, W.: Adaptive recommender systems for travel planning. Inform. Commun. Technol. Tourism 8, 1–11 (2008)

    Google Scholar 

  • Martin, B.A., Lang, B., Wong, S., Martin, B.A.: Conclusion explicitness in advertising: the moderating role of need for cognition (nfc) and argument quality (aq) on persuasion. J. Advert. 32(4), 57–66 (2003)

    Article  Google Scholar 

  • Matthews, G., Deary, I.J., Whiteman, M.C.: Personality Traits. Cambridge University Press, Cambridge (2003)

    Book  Google Scholar 

  • McInerney, J., Lacker, B., Hansen, S., Higley, K., Bouchard, H., Gruson, A., Mehrotra, R.: Explore, exploit, and explain: personalizing explainable recommendations with bandits. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 31–39 (2018)

  • Millecamp, M., Htun, N.N., Jin, Y., Verbert, K.: Controlling spotify recommendations: effects of personal characteristics on music recommender user interfaces. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 101–109 (2018)

  • Millecamp, M., Htun, N.N., Conati, C., Verbert, K.: To explain or not to explain: the effects of personal characteristics when explaining music recommendations. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 397–407 (2019a)

  • Millecamp, M., Naveed, S., Verbert, K., Ziegler, J.: To explain or not to explain: the effects of personal characteristics when explaining feature-based recommendations in different domains. In: CEUR Workshop Proceedings. CEUR (2019b)

  • Millecamp, M., Haveneers, R., Verbert, K.: Cogito ergo quid? The effect of cognitive style in a transparent mobile music recommender system. In: Proceedings of the 28th Conference on User Modeling, Adaptation and Personalization (2020a)

  • Millecamp, M., Htun, N.N., Conati, C., Verbert, K.: What’s in a user? Towards personalising explanations for music recommender interfaces. In: Proceedings of the 28th Conference on User Modeling, Adaptation and Personalization (2020b)

  • Moss, A.J., Rosenzweig, C., Robinson, J., Litman, L.: Is it ethical to use mechanical turk for behavioral research? Relevant data from a representative survey of mturk participants and wages. PsyArXiv (2020)

  • Muhammad, K., Lawlor, A., Rafter, R., Smyth, B.: Great explanations: opinionated explanations for recommendations. In: International Conference on Case-Based Reasoning, pp. 244–258. Springer (2015)

  • Müllensiefen, D., Gingras, B., Musil, J., Stewart, L.: The musicality of non-musicians: an index for assessing musical sophistication in the general population. PLoS ONE 9(2), e89642 (2014)

    Article  Google Scholar 

  • Naiseh, M., Jiang, N., Ma, J., Ali, R.: Explainable recommendations in intelligent systems: delivery methods, modalities and risks. In: International Conference on Research Challenges in Information Science, pp. 212–228. Springer (2020a)

  • Naiseh, M., Jiang, N., Ma, J., Ali, R.: Personalising explainable recommendations: literature and conceptualisation. In: World Conference on Information Systems and Technologies, pp. 518–533. Springer (2020b)

  • Naveed, S., Donkers, T., Ziegler, J.: Argumentation-based explanations in recommender systems: Conceptual framework and empirical results. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 293–298 (2018)

  • Nenkov, G.Y., Morrin, M., Schwartz, B., Ward, A., Hulland, J.: A short form of the maximization scale: factor structure, reliability and validity studies. Judgm. Decis. Mak. 3(5), 371–388 (2008)

    Google Scholar 

  • Nguyen, T.N., Ricci, F.: A chat-based group recommender system for tourism. Inform. Technol. Tourism 18(1–4), 5–28 (2018)

    Article  Google Scholar 

  • Norman, D.A., Draper, S.W.: User Centered System Design: New Perspectives on Human–Computer Interaction. CRC Press, Boca Raton (1986)

    Book  Google Scholar 

  • Nowell, L.S., Norris, J.M., White, D.E., Moules, N.J.: Thematic analysis: striving to meet the trustworthiness criteria. Int. J. Qual. Methods 16(1), 1609406917733847 (2017)

    Article  Google Scholar 

  • Nunes, M.A.S.N.: Recommender systems based on personality traits. Ph.D. thesis, Universite Montpellier II-Sciences et Techniques du Languedoc (2008)

  • Nunes, M.A.S., Hu, R.: Personality-based recommender systems: an overview. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 5–6 (2012)

  • Nunes, I., Jannach, D.: A systematic review and taxonomy of explanations in decision support and recommender systems. User Model. User-Adap. Int. 27(3–5), 393–444 (2017)

    Article  Google Scholar 

  • O’Connor, C., Joffe, H.: Intercoder reliability in qualitative research: debates and practical guidelines. Int. J. Qual. Methods 19, 1609406919899220 (2020)

  • Ones, D.S., Dilchert, S., Viswesvaran, C., Salgado, J.F.: Cognitive Abilities. Handbook of Employee Selection, pp. 255–275 (2010)

  • Pacini, R., Epstein, S.: The relation of rational and experiential information processing styles to personality, basic beliefs, and the ratio-bias phenomenon. J. Pers. Soc. Psychol. 76(6), 972 (1999)

    Article  Google Scholar 

  • Paramythis, A., Weibelzahl, S., Masthoff, J.: Layered evaluation of interactive adaptive systems: framework and formative methods. User Model. User-Adap. Int. 20(5), 383–453 (2010)

    Article  Google Scholar 

  • Paunonen, S.V., Ashton, M.C.: The nonverbal assessment of personality; the npq and the ff-npq. In: Big Five Assessment, pp. 171–190 (2002)

  • Ribeiro, M.T., Singh, S., Guestrin, C.: “ why should i trust you?” explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144 (2016)

  • Ribera, M., Lapedriza, A.: Can we do better explanations? A proposal of user-centered explainable AI. In: IUI Workshops (2019)

  • Riding, R.J.: On the nature of cognitive style. Educ. Psychol. 17(1–2), 29–49 (1997)

    Article  Google Scholar 

  • Scholz, M., Dorner, V.: Estimating optimal recommendation set sizes for individual consumers. In: Proceedings of the International Conference on Information Systems, pp. 2440–2459 (2012)

  • Springer, A., Whittaker, S.: Making transparency clear. In: Algorithmic Transparency for Emerging Technologies Workshop, p. 5 (2019a)

  • Springer, A., Whittaker, S.: Progressive disclosure: empirically motivated approaches to designing effective transparency. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 107–120 (2019b)

  • Tam, K.Y., Ho, S.Y.: Web personalization as a persuasion strategy: an elaboration likelihood model perspective. Inf. Syst. Res. 16(3), 271–291 (2005)

    Article  Google Scholar 

  • Tintarev, N., Masthoff, J.: A survey of explanations in recommender systems. In: 2007 IEEE 23rd International Conference on Data Engineering Workshop, pp. 801–810. IEEE (2007)

  • Tintarev, N., Dennis, M., Masthoff, J.: Adapting recommendation diversity to openness to experience: a study of human behaviour. In: International Conference on User Modeling, Adaptation, and Personalization, pp. 190–202. Springer (2013)

  • Tintarev, N., Masthoff, J.: Effects of individual differences in working memory on plan presentational choices. Front. Psychol. 7 (2016)

  • Tkalčič, M., Ferwerda, B., Hauger, D., Schedl, M.: Personality correlates for digital concert program notes. In: International Conference on User Modeling, Adaptation, and Personalization, pp. 364–369. Springer (2015)

  • Tong, S.T., Corriero, E.F., Matheny, R.G., Hancock, J.T.: Online daters’ willingness to use recommender technology for mate selection decisions. In: IntRS@ RecSys, pp. 45–52 (2018)

  • Tsai, C.H., Brusilovsky, P.: Beyond the ranked list: user-driven exploration and diversification of social recommendation. In: 23rd International Conference on Intelligent User Interfaces, pp. 239–250. ACM (2018)

  • Vogel, E.K., Woodman, G.F., Luck, S.J.: Storage of features, conjunctions, and objects in visual working memory. J. Exp. Psychol. Hum. Percept. Perform. 27(1), 92 (2001)

    Article  Google Scholar 

  • Völkel, S.T., Schödel, R., Buschek, D., Stachl, C., Au, Q., Bischl, B., Bühner, M., Hussmann, H.: 2 Opportunities and challenges of utilizing personality traits for personalization in HCI. In: Personalized Human–Computer Interaction, p. 31 (2019)

  • Wörndl, W., Lamche, B.: User interaction with context-aware recommender systems on smartphones. ICOM 14(1), 19–28 (2015)

    Article  Google Scholar 

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KU Leuven Research Council (Grant Agreement C24/16/017).  

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  1. Department of Computer Science, KU Leuven, Celestijnenlaan 200A bus, 3001, Leuven, Belgium

    Millecamp Martijn & Katrien Verbert

  2. Department of Computer Science, University of British Columbia (UBC), 2366 Main Mall, Vancouver, BC, Canada

    Cristina Conati

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Martijn, M., Conati, C. & Verbert, K. “Knowing me, knowing you”: personalized explanations for a music recommender system. User Model User-Adap Inter 32, 215–252 (2022). https://doi.org/10.1007/s11257-021-09304-9

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