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Personality, User Preferences and Behavior in Recommender systems

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Abstract

This paper reports on a study of 1840 users of the MovieLens recommender system with identified Big-5 personality types. Based on prior literature that suggests that personality type is a stable predictor of user preferences and behavior, we examine factors of user retention and engagement, content preferences, and rating patterns to identify recommender-system related behaviors and preferences that correlate with user personality. We find that personality traits correlate significantly with behaviors and preferences such as newcomer retention, intensity of engagement, activity types, item categories, consumption versus contribution, and rating patterns.

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Fig. 1
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Similar content being viewed by others

Notes

  1. http://movielens.org

  2. We choose these sessions to be consistent with prior work (Karumur et al. 2016b)

  3. First session in MovieLens is substantially different from other sessions since a majority of ratings are provided by most users in this session.

  4. By level of activity, we mean the number of ratings, the number of tags applied, the number of items a user adds to their wishlist, proportion of tags to ratings, number of pageviews, number of trailers viewed, extent to which trailers are viewed and so forth.

  5. We do not state all possible hypothesis combinations for every personality type as we do not have prior knowledge on their nature that suggests an expected behavior from them for certain actions in MovieLens.

  6. Trailers were a more recent feature on MovieLens. Only 401/1840 users used this feature. The results reported here, though statistically significant, are on small sample sizes for the low and high types. We provide preliminary results as trend evidence to guide future research.

  7. Per Table 4, in some of the traits we have only about 50 users on one of the sides (low or high). We chose an equal number for its counterpart as well. For instance, Agreeableness has only 65 on the ‘low’ side. We therefore pick a random sample of 50 from both the low and high sides of Agreeableness. On the other hand, a trait like Extroversion has more than 100 on both the ‘low’ and ‘high’ sides and we pick equal samples of 100 each from both sides.

References

  • Abdi, H. (2010). Holm’s sequential bonferroni procedure. In N. Salkind (ed.), Encyclopedia of research design (pp. 1–8). Thousand Oaks: Sage.

  • Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. https://doi.org/10.1109/TKDE.2005.99.

    Article  Google Scholar 

  • Allison, P. D., & Waterman, R. P. (2002). Fixed–effects negative binomial regression models. Sociological Methodology, 32(1), 247–265. https://doi.org/10.1111/1467-9531.00117.

    Article  Google Scholar 

  • Amichai-Hamburger, Y., & Vinitzky, G. (2010). Social network use and personality. Computers in Human Behavior, 26(6), 1289–1295. https://doi.org/10.1016/j.chb.2010.03.018.

    Article  Google Scholar 

  • Amichai-Hamburger, Y., Wainapel, G., & Fox, S. (2002). “On the internet no one knows I’m an introvert”: Extroversion, neuroticism, and internet interaction. Cyberpsychology & Behavior, 5(2), 125–128. https://doi.org/10.1089/109493102753770507.

    Article  Google Scholar 

  • Amiel, T., & Sargent, S. L. (2004). Individual differences in internet usage motives. Computers in Human Behavior, 20(6), 711–726. https://doi.org/10.1016/j.chb.2004.09.002.

    Article  Google Scholar 

  • Anolli, L., Villani, D., & Riva, G. (2005). Personality of people using chat: An on-line research. Cyberpsychology & Behavior, 8(1), 89–95. https://doi.org/10.1089/cpb.2005.8.89.

    Article  Google Scholar 

  • Armstrong, L., PHILLIPS, J. G., & SALING, L. L. (2000). Potential determinants of heavier internet usage. International Journal of Human-Computer Studies, 53(4), 537–550. https://doi.org/10.1006/ijhc.2000.0400.

    Article  Google Scholar 

  • Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., & Stillwell, D. (2012). Personality and patterns of Facebook usage. In Proceedings of the 4th annual ACM web science conference (pp. 24–32). New York: ACM. https://doi.org/10.1145/2380718.2380722.

  • Back, M. D., Stopfer, J. M., Vazire, S., Gaddis, S., Schmukle, S. C., Egloff, B., & Gosling, S. D. (2010). Facebook profiles reflect actual personality, not Self-Idealization. Psychological Science. https://doi.org/10.1177/0956797609360756.

    Article  Google Scholar 

  • Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44(1), 1–26. https://doi.org/10.1111/j.1744-6570.1991.tb00688.x.

    Article  Google Scholar 

  • Beaujean, A. A., & Morgan, G. B. (2016). Tutorial on using regression models with count outcomes using R. Practical Assessment, Research & Evaluation, 21(2), 1–18.

  • Block, J. (1995). A contrarian view of the five-factor approach to personality description. Psychological Bulletin, 117(2), 187–215. https://doi.org/10.1037/0033-2909.117.2.187.

    Article  Google Scholar 

  • Block, J. (2010). The five-factor framing of personality and beyond: Some ruminations. Psychological Inquiry, 21(1), 2–25. https://doi.org/10.1080/10478401003596626.

    Article  Google Scholar 

  • Borbora, Z. H., & Srivastava, J. (2012). User behavior modelling approach for churn prediction in online games. In Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom) (pp. 51–60). Presented at the privacy, security, risk and trust (PASSAT), 2012 international conference on and 2012 international Confernece on social computing (SocialCom). https://doi.org/10.1109/SocialCom-PASSAT.2012.84.

  • Braunhofer, M., Elahi, M., Ge, M., & Ricci, F. (2014). Context dependent preference acquisition with personality-based active learning in mobile recommender systems. In P. Zaphiris & A. Ioannou (Eds.), Learning and collaboration technologies. Technology-rich environments for learning and collaboration (pp. 105–116). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-07485-6_11.

    Chapter  Google Scholar 

  • Burke, M., Marlow, C., & Lento, T. (2009). Feed me: Motivating newcomer contribution in social network sites. In Proceedings of the SIGCHI conference on human factors in computing systemse (pp. 945–954). New York: ACM. https://doi.org/10.1145/1518701.1518847.

  • Butler, B. S. (2001). Membership size, communication activity, and sustainability: A resource-based model of online social structures. Information Systems Research, 12(4), 346–362. https://doi.org/10.1287/isre.12.4.346.9703.

    Article  Google Scholar 

  • Butt, S., & Phillips, J. G. (2008). Personality and self reported mobile phone use. Computers in Human Behavior, 24(2), 346–360. https://doi.org/10.1016/j.chb.2007.01.019.

    Article  Google Scholar 

  • Cantador, I., Fernández-Tobías, I., & Bellogín, A. (2013). Relating personality types with user preferences in multiple entertainment domains. https://repositorio.uam.es/handle/10486/665398. Accessed 18 Oct 2016.

  • Charlton, J. P., & Danforth, I. D. W. (2010). Validating the distinction between computer addiction and engagement: Online game playing and personality. Behaviour & Information Technology, 29(6), 601–613. https://doi.org/10.1080/01449290903401978.

    Article  Google Scholar 

  • Chen, L., Wu, W., & He, L. (2013). How personality influences users’ needs for recommendation diversity? In CHI ‘13 extended abstracts on human factors in computing systems (pp. 829–834). New York: ACM. https://doi.org/10.1145/2468356.2468505.

  • Chittaranjan, G., Blom, J., & Gatica-Perez, D. (2011). Mining large-scale smartphone data for personality studies. Personal and Ubiquitous Computing, 17(3), 433–450. https://doi.org/10.1007/s00779-011-0490-1.

    Article  Google Scholar 

  • Cobb-Clark, D. A., & Schurer, S. (2012). The stability of big-five personality traits. Economics Letters, 115(1), 11–15. https://doi.org/10.1016/j.econlet.2011.11.015.

    Article  Google Scholar 

  • Correa, T., Hinsley, A. W., & de Zúñiga, H. G. (2010). Who interacts on the web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253. https://doi.org/10.1016/j.chb.2009.09.003.

    Article  Google Scholar 

  • Cosley, D., Lam, S. K., Albert, I., Konstan, J. A., & Riedl, J. (2003). Is seeing believing?: How recommender system interfaces affect users’ opinions. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 585–592). New York: ACM. https://doi.org/10.1145/642611.642713.

  • Costa Jr., P. T., & MacCrae, R. R. (1992). Revised NEO personality inventory (NEO PI-R) and NEO five-factor inventory (NEO FFI): Professional manual. Odessa: Psychological Assessment Resources.

    Google Scholar 

  • Deniz, M. E. (2011). An investigation of decision making styles and the five-factor personality traits with respect to attachment styles. Educational Sciences: Theory & Practice, 11(1), 105–113.

    Google Scholar 

  • DeYoung, C. G., Quilty, L. C., & Peterson, J. B. (2007). Between facets and domains: 10 Aspects of the big five. Journal of Personality and Social Psychology, 93(5), 880–896. https://doi.org/10.1037/0022-3514.93.5.880.

    Article  Google Scholar 

  • Drenner, S., Sen, S., & Terveen, L. (2008). Crafting the initial user experience to achieve community goals. In Proceedings of the 2008 ACM conference on recommender systems (pp. 187–194). New York: ACM. https://doi.org/10.1145/1454008.1454039.

  • Ducheneaut, N. (2005). Socialization in an open source software community: A socio-technical analysis. Computer Supported Cooperative Work (CSCW), 14(4), 323–368. https://doi.org/10.1007/s10606-005-9000-1.

    Article  Google Scholar 

  • Dunn, G., Wiersema, J., Ham, J., & Aroyo, L. (2009). Evaluating Interface variants on personality Acquisition for Recommender Systems. In G.-J. Houben, G. McCalla, F. Pianesi, & M. Zancanaro (Eds.), User modeling, adaptation, and personalization (pp. 259–270). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-642-02247-0_25.

    Chapter  Google Scholar 

  • Dusay, J. M. (1972). Egograms and the “Constancy Hypothesis”. Transactional Analysis Bulletin, 2(3), 37–41. https://doi.org/10.1177/036215377200200313.

    Article  Google Scholar 

  • Ekstrand, M. D., Kluver, D., Harper, F. M., & Konstan, J. A. (2015). Letting users choose recommender algorithms: An experimental study. In Proceedings of the 9th ACM conference on recommender systems (pp. 11–18). New York: ACM. https://doi.org/10.1145/2792838.2800195.

  • Elahi, M., Braunhofer, M., Ricci, F., & Tkalcic, M. (2013). Personality-based active learning for collaborative filtering recommender systems. In M. Baldoni, C. Baroglio, G. Boella, & R. Micalizio (Eds.), AI*IA 2013: Advances in artificial intelligence (pp. 360–371). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-03524-6_31.

    Chapter  Google Scholar 

  • Enrich, M., Braunhofer, M., & Ricci, F. (2013). Cold-start management with cross-domain collaborative filtering and tags. In E-Commerce and Web Technologies (pp. 101–112). Presented at the International Conference on Electronic Commerce and Web Technologies, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_10.

    Google Scholar 

  • Evans, D. C., Gosling, S. D., & Carroll, A. (2008). What elements of an online social networking profile predict target-rater agreement in personality impressions? In ICWSM. AAAI: Presented at the International Conference on Weblogs and Social Media.

    Google Scholar 

  • Eysenck, H. J. (1990). Biological dimensions of personality. In Handbook of personality: Theory and research (pp. 244–276). New York: Guilford.

  • Eysenck, H., & Eysenck, M. W. (1985). Personality and Individual Differences - A Natural Science | Michael Eysenck | Springer. http://www.springer.com/us/book/9781461294702. Accessed 16 Aug 2017.

    Book  Google Scholar 

  • Fay, M. P., & Proschan, M. A. (2010). Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Statistics surveys, 4, 1–39. https://doi.org/10.1214/09-SS051.

    Article  Google Scholar 

  • Ferwerda, B., Graus, M., Vall, A., Tkalčič, M., & Schedl, M. (2016). The Influence of Users’ Personality Traits on Satisfaction And Attractiveness of Diversified Recommendation Lists. In Proceedings of the 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) 2016. Presented at the RecSys 2016, Boston, MA, USA. http://www.di.uniba.it/~swap/empire/EMPIRE16-all.pdf#page=63.

  • Fugelstad, P., Dwyer, P., Filson Moses, J., Kim, J., Mannino, C. A., Terveen, L., & Snyder, M. (2012). What Makes Users Rate (Share, Tag, Edit...)?: Predicting Patterns of Participation in Online Communities. In Proceedings of the ACM 2012 conference on computer supported cooperative work (pp. 969–978). New York: ACM. https://doi.org/10.1145/2145204.2145349.

  • Goldberg, L. R. (1992). The development of markers for the big-five factor structure. Psychological Assessment, 4(1), 26–42. https://doi.org/10.1037/1040-3590.4.1.26.

    Article  Google Scholar 

  • Goldberg, L. R., Johnson, J. A., Eber, H. W., Hogan, R., Ashton, M. C., Cloninger, C. R., & Gough, H. G. (2006). The international personality item pool and the future of public-domain personality measures. Journal of Research in Personality, 40(1), 84–96. https://doi.org/10.1016/j.jrp.2005.08.007.

    Article  Google Scholar 

  • Gosling, S. D., Rentfrow, P. J., & Swann Jr., W. B. (2003). A very brief measure of the big-five personality domains. Journal of Research in Personality, 37(6), 504–528. https://doi.org/10.1016/S0092-6566(03)00046-1.

    Article  Google Scholar 

  • Gosling, S. D., Gaddis, S., Vazire, S., & et al. (2007). Personality impressions based on Facebook profiles. International conference on weblogs and social media, 7, 1–4.

  • Hamburger, Y. A., & Ben-Artzi, E. (2000). The relationship between extraversion and neuroticism and the different uses of the internet. Computers in Human Behavior, 16(4), 441–449. https://doi.org/10.1016/S0747-5632(00)00017-0.

    Article  Google Scholar 

  • Hogan, J., & Ones, D. S. (1997). Conscientiousness and integrity at work. In R. Hogan, J. A. Johnson, & S. R. Briggs (Eds.), Handbook of personality psychology (pp. 849–870). San Diego: Academic Press.

    Chapter  Google Scholar 

  • Hu, R., & Pu, P. (2010). A study on user perception of personality-based recommender systems. In P. D. Bra, A. Kobsa, & D. Chin (Eds.), User modeling, adaptation, and personalization (pp. 291–302). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-642-13470-8_27.

    Chapter  Google Scholar 

  • Hu, R., & Pu, P. (2011). Enhancing collaborative filtering systems with personality information. In Proceedings of the fifth ACM conference on recommender systems (pp. 197–204). New York: ACM. https://doi.org/10.1145/2043932.2043969.

  • Hu, R., & Pu, P. (2013). Exploring relations between personality and user rating behaviors. In UMAP Workshops, Rome, Italy.

  • Hu, L., Cao, J., Xu, G., Cao, L., Gu, Z., & Zhu, C. (2013). Personalized recommendation via cross-domain triadic factorization. In Proceedings of the 22Nd international conference on world wide web (pp. 595–606). New York: ACM. https://doi.org/10.1145/2488388.2488441.

  • John, O. (1990). The “big five” factor taxonomy: Dimensions of personality in the natural language and in questionnaires. In L. Pervin (Ed.), Handbook of personality: Theory and research (Vol. 14, pp. 66–100). Guilford: New York.

    Google Scholar 

  • John, O. P., & Srivastava, S. (1999). The big five trait taxonomy: History, measurement, and theoretical perspectives. In Handbook of Personality: Theory and Research (second., pp. 102–138). The Guildford press. https://books.google.com/books?hl=en&lr=&id=iXMQq7wg-qkC&oi=fnd&pg=PA102&dq=the+big+five+trait+taxonomy+&ots=uDd926gzdg&sig=kxThw6Z9m3yNp9KbNSkRKv7nN0Y#v=onepage&q=the%20big%20five%20trait%20taxonomy&f=false.

  • Judge, T. A., & Ilies, R. (2002). Relationship of personality to performance motivation: A meta-analytic review. Journal of Applied Psychology, 87(4), 797–807. https://doi.org/10.1037/0021-9010.87.4.797.

    Article  Google Scholar 

  • Kairam, S. R., Wang, D. J., & Leskovec, J. (2012). The life and death of online groups: Predicting group growth and longevity. In Proceedings of the fifth ACM international conference on web search and data mining (pp. 673–682). New York: ACM. https://doi.org/10.1145/2124295.2124374.

  • Karumur, R. P., & Konstan, J. A. (2016). Relating newcomer personality to survival and activity in recommender systems. In Proceedings of the 2016 conference on user modeling adaptation and personalization (pp. 195–205). New York: ACM. https://doi.org/10.1145/2930238.2930246.

  • Karumur, R. P., Nguyen, T. T., & Konstan, J. A. (2016a). Exploring the value of personality in predicting rating behaviors: A study of category preferences on MovieLens. In Proceedings of the 10th ACM conference on recommender systems (pp. 139–142). New York: ACM. https://doi.org/10.1145/2959100.2959140.

  • Karumur, R. P., Nguyen, T. T., & Konstan, J. A. (2016b). Early activity diversity: Assessing newcomer retention from first-session activity. In Proceedings of the 19th ACM conference on Computer-Supported Cooperative Work & Social Computing (pp. 595–608). New York: ACM. https://doi.org/10.1145/2818048.2820009.

  • Kaufman, J. C., & Simonton, D. K. (2014). The social science of cinema. New York: OUP USA.

    Google Scholar 

  • Kilman, R. H., & Thomas, K. W. (1974). Thomas-Kilmann conflict mode instrument. Xicom, Incorporated: Santa Clara.

    Google Scholar 

  • Kompan, M., & Bieliková, M. (2014). Social structure and personality enhanced group recommendation. In UMAP Workshops. Presented at the User Modeling, Adaptation and Personalization, Aalborg, Denmark. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.457.1511&rep=rep1&type=pdf.

  • Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805. https://doi.org/10.1073/pnas.1218772110.

    Article  Google Scholar 

  • Kraaykamp, G. (2009). Parents, personality and media preferences. Communications, 26(1), 15–38. https://doi.org/10.1515/comm.2001.26.1.15.

    Article  Google Scholar 

  • Kraaykamp, G., & Eijck, K. v. (2005). Personality, media preferences, and cultural participation. Personality and Individual Differences, 38(7), 1675–1688. https://doi.org/10.1016/j.paid.2004.11.002.

    Article  Google Scholar 

  • Krishnaraju, V., Mathew, S. K., & Sugumaran, V. (2016). Web personalization for user acceptance of technology: An empirical investigation of E-government services. Information Systems Frontiers, 18(3), 579–595. https://doi.org/10.1007/s10796-015-9550-9.

    Article  Google Scholar 

  • Lambert, D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34(1), 1–14. https://doi.org/10.1080/00401706.1992.10485228.

    Article  Google Scholar 

  • Landers, R. N., & Lounsbury, J. W. (2006). An investigation of big five and narrow personality traits in relation to internet usage. Computers in Human Behavior, 22(2), 283–293. https://doi.org/10.1016/j.chb.2004.06.001.

    Article  Google Scholar 

  • Li, B., Yang, Q., & Xue, X. (2009a). Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction. In IJCAI (Vol. 9, pp. 2052–2057). Presented at the International Joint Conference on Artificial Intelligence. http://www.aaai.org/ocs/index.php/IJCAI/IJCAI-09/paper/download/403/819.

  • Li, B., Yang, Q., & Xue, X. (2009b). Transfer learning for collaborative filtering via a rating-matrix generative model. In Proceedings of the 26th annual international conference on machine learning (pp. 617–624). New York: ACM. https://doi.org/10.1145/1553374.1553454.

  • Ling, K., Beenen, G., Ludford, P., Wang, X., Chang, K., Li, X., et al. (2005). Using social psychology to motivate contributions to online communities. Journal of Computer-Mediated Communication, 10(4), 00–00. https://doi.org/10.1111/j.1083-6101.2005.tb00273.x.

    Article  Google Scholar 

  • Lowell, E., & Conley, J. J. (1987). Personality and compatibility: A prospective analysis of marital stability and marital satisfaction. Journal of Personality and Social Psychology, 52(1), 27–40. https://doi.org/10.1037/0022-3514.52.1.27.

    Article  Google Scholar 

  • Manca, M., Boratto, L., & Carta, S. (2015). Behavioral data mining to produce novel and serendipitous friend recommendations in a social bookmarking system. Information Systems Frontiers, 1–15. https://doi.org/10.1007/s10796-015-9600-3.

    Article  Google Scholar 

  • Mark, G., & Ganzach, Y. (2014). Personality and internet usage: A large-scale representative study of young adults. Computers in Human Behavior, 36, 274–281. https://doi.org/10.1016/j.chb.2014.03.060.

    Article  Google Scholar 

  • McCrae, R. R., & Allik, I. (2002). The five-factor model of personality across cultures. Berlin: Springer Science & Business Media.

    Book  Google Scholar 

  • McCrae, R. R., & John, O. P. (1992). An introduction to the five-factor model and its applications. Journal of Personality, 60(2), 175–215. https://doi.org/10.1111/j.1467-6494.1992.tb00970.x.

    Article  Google Scholar 

  • McLure Wasko, M., & Faraj, S. (2000). “It is what one does”: Why people participate and help others in electronic communities of practice. The Journal of Strategic Information Systems, 9(2–3), 155–173. https://doi.org/10.1016/S0963-8687(00)00045-7.

    Article  Google Scholar 

  • McNee, S. M., Riedl, J., & Konstan, J. A. (2006). Being accurate is not enough: How accuracy metrics have hurt recommender systems. In CHI ‘06 extended abstracts on human factors in computing systems (pp. 1097–1101). New York: ACM. https://doi.org/10.1145/1125451.1125659.

  • Moreno, O., Shapira, B., Rokach, L., & Shani, G. (2012). TALMUD: Transfer learning for multiple domains. In Proceedings of the 21st ACM international conference on information and knowledge management (pp. 425–434). New York: ACM. https://doi.org/10.1145/2396761.2396817.

  • Muller, M., Shami, N. S., Millen, D. R., & Feinberg, J. (2010). We are all lurkers: Consuming behaviors among authors and readers in an Enterprise file-sharing service. In Proceedings of the 16th ACM international conference on supporting group work (pp. 201–210). New York: ACM. https://doi.org/10.1145/1880071.1880106.

  • Nguyen, T. T., Kluver, D., Wang, T.-Y., Hui, P.-M., Ekstrand, M. D., Willemsen, M. C., & Riedl, J. (2013). Rating support interfaces to improve user experience and recommender accuracy. In Proceedings of the 7th ACM conference on recommender systems (pp. 149–156). New York: ACM. https://doi.org/10.1145/2507157.2507188.

  • Nov, O. (2007). What motivates Wikipedians? Communications of the ACM, 50(11), 60–64. https://doi.org/10.1145/1297797.1297798.

    Article  Google Scholar 

  • Onori, M., Micarelli, A., & Sansonetti, G. (2016). A Comparative Analysis of Personality-Based Music Recommender Systems. In Proceedings of the 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) 2016. Presented at the RecSys 2016, Boston, MA, USA. http://www.di.uniba.it/~swap/empire/EMPIRE16-all.pdf#page=63.

  • Orchard, L. J., & Fullwood, C. (2009). Current perspectives on personality and internet use. Social Science Computer Review. https://doi.org/10.1177/0894439309335115.

    Article  Google Scholar 

  • Pal, A., Chang, S., & Konstan, J. A. (2012). Evolution of experts in question answering communities. In Sixth International AAAI Conference on Weblogs and Social Media. Presented at the Sixth International AAAI Conference on Weblogs and Social Media. http://www.aaai.org/ocs/index.php/ICWSM/ICWSM12/paper/view/4653. Accessed 24 Oct 2016.

  • Pan, W., Liu, N. N., Xiang, E. W., & Yang, Q. (2011). Transfer learning to predict missing ratings via heterogeneous user feedbacks. In IJCAI (Vol. 22 (3), p. 2318). Presented at the IJCAI proceedings-international joint conference on artificial intelligence.

  • Panciera, K., Halfaker, A., & Terveen, L. (2009). Wikipedians are born, not made: A study of power editors on Wikipedia. In Proceedings of the ACM 2009 international conference on supporting group work (pp. 51–60). New York: ACM. https://doi.org/10.1145/1531674.1531682.

  • Phillips, J. G., Butt, S., & Blaszczynski, A. (2006). Personality and self-reported use of mobile phones for games. Cyberpsychology & Behavior, 9(6), 753–758. https://doi.org/10.1089/cpb.2006.9.753.

    Article  Google Scholar 

  • Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011). Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. In 2011 I.E. Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 I.E. Third Inernational Conference on Social Computing (SocialCom) (pp. 180–185). Presented at the 2011 I.E. Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 I.E. Third Inernational Conference on Social Computing (SocialCom). https://doi.org/10.1109/PASSAT/SocialCom.2011.26.

  • Quijano-Sánchez, L., Bridge, D., Díaz-Agudo, B., & Recio-García, J. A. (2012). A case-based solution to the cold-start problem in group recommenders. In Case-Based Reasoning Research and Development (pp. 342–356). Presented at the International Conference on Case-Based Reasoning, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32986-9_26.

    Chapter  Google Scholar 

  • Quijano-Sánchez, L., Recio-Garcia, J. A., Diaz-Agudo, B., & Jimenez-Diaz, G. (2013). Social factors in group recommender systems. ACM Transactions on Intelligent Systems and Technology, 4(1), 8:1–8:30. https://doi.org/10.1145/2414425.2414433.

    Article  Google Scholar 

  • Rashid, A. M., Albert, I., Cosley, D., Lam, S. K., McNee, S. M., Konstan, J. A., & Riedl, J. (2002). Getting to know you: Learning new user preferences in recommender systems. In Proceedings of the 7th international conference on intelligent user interfaces (pp. 127–134). New York: ACM. https://doi.org/10.1145/502716.502737.

  • Recio-Garcia, J. A., Jimenez-Diaz, G., Sanchez-Ruiz, A. A., & Diaz-Agudo, B. (2009). Personality aware recommendations to groups. In Proceedings of the third ACM conference on recommender systems (pp. 325–328). New York: ACM. https://doi.org/10.1145/1639714.1639779.

  • Rentfrow, P. J., & Gosling, S. D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236–1256. https://doi.org/10.1037/0022-3514.84.6.1236.

    Article  Google Scholar 

  • Rentfrow, P. J., Goldberg, L. R., & Zilca, R. (2011). Listening, watching, and reading: The structure and correlates of entertainment preferences. Journal of Personality, 79(2), 223–258. https://doi.org/10.1111/j.1467-6494.2010.00662.x.

    Article  Google Scholar 

  • Rong, W., Peng, B., Ouyang, Y., Liu, K., & Xiong, Z. (2015). Collaborative personal profiling for web service ranking and recommendation. Information Systems Frontiers, 17(6), 1265–1282. https://doi.org/10.1007/s10796-014-9495-4.

    Article  Google Scholar 

  • Ross, C., Orr, E. S., Sisic, M., Arseneault, J. M., Simmering, M. G., & Orr, R. R. (2009). Personality and motivations associated with Facebook use. Computers in Human Behavior, 25(2), 578–586. https://doi.org/10.1016/j.chb.2008.12.024.

    Article  Google Scholar 

  • Ryan, T., & Xenos, S. (2011). Who uses Facebook? An investigation into the relationship between the big five, shyness, narcissism, loneliness, and Facebook usage. Computers in Human Behavior, 27(5), 1658–1664. https://doi.org/10.1016/j.chb.2011.02.004.

    Article  Google Scholar 

  • Schmitt, D. P., Allik, J., McCrae, R. R., & Benet-Martínez, V. (2007). The geographic distribution of big five personality traits patterns and profiles of human self-description across 56 nations. Journal of Cross-Cultural Psychology, 38(2), 173–212. https://doi.org/10.1177/0022022106297299.

    Article  Google Scholar 

  • Schrammel, J., Köffel, C., & Tscheligi, M. (2009). Personality traits, usage patterns and information disclosure in online communities. In Proceedings of the 23rd British HCI group annual conference on people and computers: Celebrating people and technology (pp. 169–174). Swinton: British Computer Society. http://dl.acm.org/citation.cfm?id=1671011.1671031. Accessed 18 Oct 2016.

  • Selfhout, M., Burk, W., Branje, S., Denissen, J., Van Aken, M., & Meeus, W. (2010). Emerging late adolescent friendship networks and big five personality traits: A social network approach. Journal of Personality, 78(2), 509–538. https://doi.org/10.1111/j.1467-6494.2010.00625.x.

    Article  Google Scholar 

  • Sen, S., Lam, S. K., Rashid, A. M., Cosley, D., Frankowski, D., Osterhouse, J., et al. (2006). Tagging, communities, vocabulary, evolution. In Proceedings of the 2006 20th anniversary conference on computer supported cooperative work (pp. 181–190). New York: ACM. https://doi.org/10.1145/1180875.1180904.

  • Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3–55. https://doi.org/10.1145/584091.584093.

    Article  Google Scholar 

  • Shen, J., Brdiczka, O., & Liu, J. (2013). Understanding email writers: Personality prediction from email messages. In S. Carberry, S. Weibelzahl, A. Micarelli, & G. Semeraro (Eds.), User modeling, adaptation, and personalization (pp. 318–330). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-642-38844-6_29.

    Chapter  Google Scholar 

  • Shi, Y., Larson, M., & Hanjalic, A. (2011). Tags as bridges between domains: Improving recommendation with tag-induced cross-domain collaborative filtering. In User modeling, adaption and personalization (pp. 305–316). Springer. https://doi.org/10.1007/978-3-642-22362-4_26.

    Chapter  Google Scholar 

  • Simpson, E. H. (1949). Measurement of diversity. Nature, 163, 688. https://doi.org/10.1038/163688a0.

    Article  Google Scholar 

  • Swickert, R. J., Hittner, J. B., Harris, J. L., & Herring, J. A. (2002). Relationships among internet use, personality, and social support. Computers in Human Behavior, 18(4), 437–451. https://doi.org/10.1016/S0747-5632(01)00054-1.

    Article  Google Scholar 

  • Teng, C.-I. (2008). Personality differences between online game players and nonplayers in a student sample. Cyberpsychology & Behavior, 11(2), 232–234. https://doi.org/10.1089/cpb.2007.0064.

    Article  Google Scholar 

  • Thomas, K. W. (1992). Conflict and conflict management: Reflections and update. Journal of Organizational Behavior, 13(3), 265–274. https://doi.org/10.1002/job.4030130307.

    Article  Google Scholar 

  • Tiroshi, A., Berkovsky, S., Kaafar, M. A., Chen, T., & Kuflik, T. (2013). Cross social networks interests predictions based on graph features. In Proceedings of the 7th ACM conference on recommender systems (pp. 319–322). New York: ACM. https://doi.org/10.1145/2507157.2507206.

  • Tkalčič, M., & Chen, L. (2015). Personality and recommender systems. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 715–739). Boston: Springer US. https://doi.org/10.1007/978-1-4899-7637-6_21.

    Chapter  Google Scholar 

  • Tkalčič, M., Kunaver, M., Tasic, J., & Košir, A. (2009). Personality based user similarity measure for a collaborative recommender system. In Proceedings of the 5th Workshop on Emotion in Human-Computer Interaction-Real world challenges (pp. 30–37).

  • Tkalčič, M., Burnik, U., & Košir, A. (2010). Using affective parameters in a content-based recommender system for images. User Modeling and User-Adapted Interaction, 20(4), 279–311. https://doi.org/10.1007/s11257-010-9079-z.

    Article  Google Scholar 

  • Tosun, L. P., & Lajunen, T. (2010). Does internet use reflect your personality? Relationship between Eysenck’s personality dimensions and internet use. Computers in Human Behavior, 26(2), 162–167. https://doi.org/10.1016/j.chb.2009.10.010.

    Article  Google Scholar 

  • Tupes, E. C., & Christal, R. E. (1992). Recurrent personality factors based on trait ratings. Journal of Personality, 60(2), 225–251. https://doi.org/10.1111/j.1467-6494.1992.tb00973.x.

    Article  Google Scholar 

  • Turner, T. C., Smith, M. A., Fisher, D., & Welser, H. T. (2005). Picturing Usenet: Mapping computer-mediated collective action. Journal of Computer-Mediated Communication, 10(4), 00–00. https://doi.org/10.1111/j.1083-6101.2005.tb00270.x.

    Article  Google Scholar 

  • Tuten, T. L., & Bosnjak, M. (2001). Understanding differences in web usage: The role of need for cognition and the five factor model of personality. Social Behavior and Personality: An International Journal, 29(4), 391–398. https://doi.org/10.2224/sbp.2001.29.4.391.

    Article  Google Scholar 

  • van Lankveld, G., Spronck, P., van den Herik, J., & Arntz, A. (2011). Games as personality profiling tools. In 2011 I.E. Conference on Computational Intelligence and Games (CIG’11) (pp. 197–202). Presented at the 2011 I.E. conference on computational intelligence and games (CIG’11). https://doi.org/10.1109/CIG.2011.6032007.

  • Welser, H. T., Gleave, E., Fisher, D., & Smith, M. (2007). Visualizing the signatures of social roles in online discussion groups. Journal of Social Structure, 8(2), 1–32.

    Google Scholar 

  • Winoto, P., & Tang, T. (2008). If you like the devil wears Prada the book, will you also enjoy the devil wears Prada the movie? A study of cross-domain recommendations. New Generation Computing, 26(3), 209–225. https://doi.org/10.1007/s00354-008-0041-0.

    Article  Google Scholar 

  • Winter, B. (2013). Linear models and linear mixed effects models in R with linguistic applications. arXiv: 1308.5499. http://arxiv.org/pdf/1308.5499.pdf. Accessed 26 Oct 2016.

  • Wolfradt, U., & Doll, J. (2001). Motives of adolescents to use the internet as a function of personality traits, personal and social factors. Journal of Educational Computing Research, 24(1), 13–27. https://doi.org/10.2190/ANPM-LN97-AUT2-D2EJ.

    Article  Google Scholar 

  • Wu, W., & Chen, L. (2015). Implicit Acquisition of User Personality for augmenting movie recommendations. In F. Ricci, K. Bontcheva, O. Conlan, & S. Lawless (Eds.), User modeling, adaptation and personalization (pp. 302–314). Springer International Publishing: Cham. https://doi.org/10.1007/978-3-319-20267-9_25.

    Chapter  Google Scholar 

  • Wu, W., Chen, L., & He, L. (2013). Using personality to adjust diversity in recommender systems. In Proceedings of the 24th ACM conference on hypertext and social media (pp. 225–229). New York: ACM. https://doi.org/10.1145/2481492.2481521.

  • Yan, X., Wang, J., & Chau, M. (2015). Customer revisit intention to restaurants: Evidence from online reviews. Information Systems Frontiers, 17(3), 645–657. https://doi.org/10.1007/s10796-013-9446-5.

    Article  Google Scholar 

  • Yang, J., Wei, X., Ackerman, M. S., & Adamic, L. A. (2010). Activity lifespan: An analysis of user survival patterns in online knowledge sharing communities. International conference on weblogs and social media, 10, 186–193.

  • Zhuang, F., Luo, P., Xiong, H., Xiong, Y., He, Q., & Shi, Z. (2010). Cross-domain learning from multiple sources: A consensus regularization perspective. IEEE Transactions on Knowledge and Data Engineering, 22(12), 1664–1678. https://doi.org/10.1109/TKDE.2009.205.

    Article  Google Scholar 

  • Zuckerman, M., Ulrich, R. S., & McLaughlin, J. (1993). Sensation seeking and reactions to nature paintings. Personality and Individual Differences, 15(5), 563–576. https://doi.org/10.1016/0191-8869(93)90340-9.

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Science Foundation grant IIS-1319382. We thank MovieLens users who took the Personality survey. We also thank Max Harper, Isaac Johnson, Daniel Kluver, Vikas Kumar, Colleen Smith, Lana Yarosh, and Qian Zhao of the GroupLens Research lab for their occasional valuable inputs and feedback on this work. We also thank our reviewers for their valuable suggestions.

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Correspondence to Raghav Pavan Karumur.

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Karumur, R.P., Nguyen, T.T. & Konstan, J.A. Personality, User Preferences and Behavior in Recommender systems. Inf Syst Front 20, 1241–1265 (2018). https://doi.org/10.1007/s10796-017-9800-0

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