Personality-Aware Collaborative Learning: Models and Explanations

  • Yong ZhengEmail author
  • Archana Subramaniyan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 926)


Personality traits have been demonstrated as one of the effective human factors in the process of decision making. Personality-aware recommendation models have been built for different applications. However, the models and research for educations are still under investigation. In this paper, we utilize the educational learning as a case study, exploit and summarize different approaches which take advantage of personality traits in collaborative personalized recommendations. Furthermore, we extend the existing personality-aware recommendation models and propose two other alternative recommendation algorithms which can utilize the personality traits. We perform empirical comparisons and studies over an educational data set, and explain the effects of personality from the perspective of the characteristics of each algorithm, in order to discover more insights in the data and models.


  1. 1.
    Baltrunas, L., Ricci, F.: Context-based splitting of item ratings in collaborative filtering. In: Proceedings of ACM Conference on Recommender Systems, pp. 245–248 (2009)Google Scholar
  2. 2.
    Bian, L., Holtzman, H.: Online friend recommendation through personality matching and collaborative filtering. In: Proceedings of UBICOMM, pp. 230–235 (2011)Google Scholar
  3. 3.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  4. 4.
    Burke, R.: Hybrid recommender systems: survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)CrossRefGoogle Scholar
  5. 5.
    Burke, R., Zheng, Y., Riley, S.: Experience discovery: hybrid recommendation of student activities using social network data. In: Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems, pp. 49–52. ACM (2011)Google Scholar
  6. 6.
    Chen, L., Wu, W., He, L.: How personality influences users’ needs for recommendation diversity? In: Extended Abstracts on Human Factors in Computing Systems, CHI 2013, pp. 829–834. ACM (2013)Google Scholar
  7. 7.
    Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Recommender Systems Handbook, pp. 421–451. Springer (2015)Google Scholar
  8. 8.
    Elahi, M., Braunhofer, M., Ricci, F., Tkalcic, M.: Personality-based active learning for collaborative filtering recommender systems. In: Congress of the Italian Association for Artificial Intelligence, pp. 360–371. Springer (2013)Google Scholar
  9. 9.
    Ferwerda, B., Yang, E., Schedl, M., Tkalcic, M.: Personality traits predict music taxonomy preferences. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2241–2246. ACM (2015)Google Scholar
  10. 10.
    Gosling, S.D., Rentfrow, P.J., Swann Jr., W.B.: A very brief measure of the Big-Five personality domains. J. Res. Pers. 37, 504–528 (2003)CrossRefGoogle Scholar
  11. 11.
    He, Q., Pei, J., Kifer, D., Mitra, P., Giles, L.: Context-aware citation recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 421–430. ACM (2010)Google Scholar
  12. 12.
    Hu, R., Pu, P.: Using personality information in collaborative filtering for new users. In: Recommender Systems and the Social Web, p. 17 (2010)Google Scholar
  13. 13.
    Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422–446 (2002)CrossRefGoogle Scholar
  14. 14.
    Komarraju, M., Karau, S.J., Schmeck, R.R., Avdic, A.: The Big Five personality traits, learning styles, and academic achievement. Pers. Individ. Differ. 51(4), 472–477 (2011)CrossRefGoogle Scholar
  15. 15.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)CrossRefGoogle Scholar
  16. 16.
    Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. In: Recommender Systems Handbook, pp. 73–105. Springer (2011)Google Scholar
  17. 17.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender systems in technology enhanced learning. In: Recommender Systems Handbook, pp. 387–415. Springer (2011)Google Scholar
  18. 18.
    Massa, P., Avesani, P.: Trust-aware recommender systems. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 17–24. ACM (2007)Google Scholar
  19. 19.
    McCrae, R.R., John, O.P.: An introduction to the five-factor model and its applications. J. Pers. 60(2), 175–215 (1992)CrossRefGoogle Scholar
  20. 20.
    Nunes, M.S., Hu, R.: Personality-based recommender systems: an overview. In: Proceedings of ACM Conference on Recommender Systems, pp. 5–6 (2012)Google Scholar
  21. 21.
    Pera, M.S., Ng, Y.-K.: What to read next?: making personalized book recommendations for K-12 users. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 113–120. ACM (2013)Google Scholar
  22. 22.
    Said, A., De Luca, E.W., Albayrak, S.: Inferring contextual user profiles – improving recommender performance. In: ACM RecSys, the 4th Workshop on Context-Aware Recommender Systems (2011)Google Scholar
  23. 23.
    Tkalcic, M., Kunaver, M., Tasic, J., Košir, A.: 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 (2009)Google Scholar
  24. 24.
    Wu, W., Chen, L., He, L.: Using personality to adjust diversity in recommender systems. In: Proceedings of the 24th ACM Conference on Hypertext and Social Media, pp. 225–229. ACM (2013)Google Scholar
  25. 25.
    Zheng, Y.: Adapt to emotional reactions in context-aware personalization. In: EMPIRE Workshop @ ACM Conference on Recommender Systems (2016)Google Scholar
  26. 26.
    Zheng, Y.: Affective prediction by collaborative chains in movie recommendation. In: Proceedings of the International Conference on Web Intelligence, pp. 815–822. ACM (2017)Google Scholar
  27. 27.
    Zheng, Y.: Criteria chains: a novel multi-criteria recommendation approach. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces, pp. 29–33. ACM (2017)Google Scholar
  28. 28.
    Zheng, Y.: Exploring user roles in group recommendations: a learning approach. In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization, pp. 49–52. ACM (2018)Google Scholar
  29. 29.
    Zheng, Y.: Identifying dominators and followers in group decision making based on the personality traits. In: Companion Proceedings of the 23rd International on Intelligent User Interfaces: 2nd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (2018)Google Scholar
  30. 30.
    Zheng, Y.: Personality-aware decision making in educational learning. In: Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion. ACM (2018)Google Scholar
  31. 31.
    Zheng, Y.: Utility-based multi-criteria recommender systems. In: Proceedings of the 34th Annual ACM Symposium on Applied Computing. ACM (2019)Google Scholar
  32. 32.
    Zheng, Y., Burke, R., Mobasher, B.: Splitting approaches for context-aware recommendation: an empirical study. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 274–279. ACM (2014)Google Scholar
  33. 33.
    Zheng, Y., Mobasher, B., Burke, R.: Emotions in context-aware recommender systems. In: Emotions and Personality in Personalized Services, pp. 311–326. Springer (2016)Google Scholar
  34. 34.
    Zheng, Y., Mobasher, B., Burke, R.D.: The role of emotions in context-aware recommendation. In: Proceedings of the 3rd Workshop on Human Decision Making in Recommender Systems @ ACM RecSys, pp. 21–28 (2013)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Illinois Institute of TechnologyChicagoUSA

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