Alleviating the new user problem in collaborative filtering by exploiting personality information

  • Ignacio Fernández-Tobías
  • Matthias Braunhofer
  • Mehdi Elahi
  • Francesco Ricci
  • Iván Cantador
Article

Abstract

The new user problem in recommender systems is still challenging, and there is not yet a unique solution that can be applied in any domain or situation. In this paper we analyze viable solutions to the new user problem in collaborative filtering (CF) that are based on the exploitation of user personality information: (a) personality-based CF, which directly improves the recommendation prediction model by incorporating user personality information, (b) personality-based active learning, which utilizes personality information for identifying additional useful preference data in the target recommendation domain to be elicited from the user, and (c) personality-based cross-domain recommendation, which exploits personality information to better use user preference data from auxiliary domains which can be used to compensate the lack of user preference data in the target domain. We benchmark the effectiveness of these methods on large datasets that span several domains, namely movies, music and books. Our results show that personality-aware methods achieve performance improvements that range from 6 to 94 % for users completely new to the system, while increasing the novelty of the recommended items by 3–40 % with respect to the non-personalized popularity baseline. We also discuss the limitations of our approach and the situations in which the proposed methods can be better applied, hence providing guidelines for researchers and practitioners in the field.

Keywords

Recommender systems Collaborative filtering User personality Cold-start Cross-domain Active learning 

References

  1. Abel, F., Herder, E., Houben, G.-J., Henze, N., Krause, D.: Cross-system user modeling and personalization on the social web. User Model. User Adapt. Interact. 23(2–3), 169–209 (2013)CrossRefGoogle Scholar
  2. Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., Stillwell, D.: Personality and patterns of facebook usage. In: Proceedings of the 3rd Annual ACM Web Science Conference, pp. 24–32. ACM (2012)Google Scholar
  3. Berkovsky, S., Kuflik, T., Ricci, F.: Mediation of user models for enhanced personalization in recommender systems. User Model. User Adapt. Interact. 18(3), 245–286 (2008)CrossRefGoogle Scholar
  4. Braunhofer, M., Elahi, M., Ge, M., Ricci, F.: Context dependent preference acquisition with personality-based active learning in mobile recommender systems. In: Learning and Collaboration Technologies. Technology-Rich Environments for Learning and Collaboration, pp. 105–116. Springer (2014a)Google Scholar
  5. Braunhofer, M., Elahi, M., Ricci, F.: Techniques for cold-starting context-aware mobile recommender systems for tourism. Intell. Artif. 8(2), 129–143 (2014b)Google Scholar
  6. Braunhofer, M., Elahi, M., Ricci, F.: User personality and the new user problem in a context-aware point of interest recommender system. In: Information and Communication Technologies in Tourism 2015, pp. 537–549. Springer, Lugano (2015)Google Scholar
  7. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers, Inc., San Francisco (1998)Google Scholar
  8. Burger, J.M.: Personality. Wadsworth Publishing (2010)Google Scholar
  9. Cantador, I., Cremonesi, P.: Tutorial on cross-domain recommender systems. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 401–402. ACM (2014)Google Scholar
  10. Cantador, I., Fernández-Tobías, I., Bellogín, A., Kosinski, M., Stillwell, D.: Relating personality types with user preferences in multiple entertainment domains. In: UMAP’13 Workshops. Springer (2013)Google Scholar
  11. Cantador, I., Konstas, I., Jose, J.M.: Categorising social tags to improve folksonomy-based recommendations. J. Web Semant. 9(1), 1–15 (2010)CrossRefGoogle Scholar
  12. Cantador, I., Fernández-Tobías, I., Berkovsky, S., Cremonesi, P.: Cross-domain recommender systems. In: Recommender Systems Handbook, 2nd edn, pp. 919–959. Springer (2015)Google Scholar
  13. Carenini, G., Smith, J., Poole, D.: Towards more conversational and collaborative recommender systems. In: Proceedings of the 8th International Conference on Intelligent User Interfaces, pp. 12–18. ACM, New York (2003)Google Scholar
  14. Chausson, O.: Who Watches What? Assessing the Impact of Gender and Personality on Film Preferences (2010)Google Scholar
  15. Costa, P.T., McCrae, R.R.: Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO FFI): Professional Manual. Psychological Assessment Resources, Odessa (1992)Google Scholar
  16. Cremonesi, P., Tripodi, A., Turrin, R.: Cross-domain recommender systems. In: Proceedings of the 11th International Conference on Data Mining Workshops, pp. 496–503 (2011)Google Scholar
  17. Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 107–144. Springer (2011)Google Scholar
  18. Dunn, G., Wiersema, J., Ham, J., Aroyo, L.: Evaluating interface variants on personality acquisition for recommender systems. In: User Modeling, Adaptation, and Personalization, pp. 259–270. Springer, Berlin (2009)Google Scholar
  19. Elahi, M., Braunhofer, M., Ricci, F., Tkalcic, M.: Personality-based active learning for collaborative filtering recommender systems. In: The 12th Symposium of the Italian Association for Artificial Intelligence, vol. 8249, pp. 360–371. Springer (2013)Google Scholar
  20. Elahi, M., Repsys, V., Ricci, F.: Rating elicitation strategies for collaborative filtering. In: Huemer, C., Setzer, T. (eds.) Proceedings of the 12th International Conference on E-Commerce and Web Technologies, vol. 85, pp. 160–171. Springer (2011)Google Scholar
  21. Elahi, M., Ricci, F., Rubens, N.: Adapting to natural rating acquisition with combined active learning strategies. In: Proceedings of the 20th International Conference on Foundations of Intelligent Systems, pp. 254–263. Springer (2012)Google Scholar
  22. Elahi, M., Ricci, F., Rubens, N.: Active learning in collaborative filtering recommender systems. In: E-Commerce and Web Technologies, pp. 113–124. Springer (2014a)Google Scholar
  23. Elahi, M., Ricci, F., Rubens, N.: Active learning strategies for rating elicitation in collaborative filtering: a system-wide perspective. ACM Trans. Interact. Intell. Syst. 5(1), 13:1–13:33 (2014b)Google Scholar
  24. Enrich, M., Braunhofer, M., Ricci, F.: Cold-start management with cross-domain collaborative filtering and tags. In: Proceedings of the 14th International Conference on E-Commerce and Web Technologies, pp. 101–112 (2013)Google Scholar
  25. Farnadi, G., Sitaraman, G., Sushmita, S., Celli, F., Kosinski, M., Stillwell, D., Davalos, S., Moens, M.-F., De Cock, M.: Computational personality recognition in social media. In: User Modeling, Adaptation, and Personalization—Special Issue on Personality in Personalized Systems. Springer (2016)Google Scholar
  26. Fernández-Tobías, I., Cantador, I., Kaminskas, M., Ricci, F.: Cross-domain recommender systems: a survey of the state of the art. In: Proceedings of the 2nd Spanish Conference on Information Retrieval, pp. 187–198 (2012)Google Scholar
  27. Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., Guo, J.: Cross-domain recommendation via cluster-level latent factor model. In: Proceedings of the 2013 European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 161–176 (2013)Google Scholar
  28. Golbandi, N., Koren, Y., Lempel, R.: On bootstrapping recommender systems. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1805–1808. ACM, New York (2010)Google Scholar
  29. Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 595–604. ACM, New York (2011)Google Scholar
  30. Goldberg, L.R., Johnson, J.A., Eber, H.W., Hogan, R., Ashton, M.C., Robert Cloninger, C., Gough, H.G.: The international personality item pool and the future of public-domain personality measures. J. Res. Personal. 40(1), 84–96 (2006)CrossRefGoogle Scholar
  31. Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237. ACM, New York (1999)Google Scholar
  32. Hu, R., Pu, P.: A comparative user study on rating vs. personality quiz based preference elicitation methods. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, pp. 367–372. ACM, New York (2009)Google Scholar
  33. Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Proceedings of the 5th ACM Conference on Recommender Systems, pp. 197–204. ACM, New York (2011)Google Scholar
  34. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th IEEE International Conference on Data Mining, pp. 263–272. IEEE, Washington, DC (2008)Google Scholar
  35. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press, Cambridge (2010)CrossRefGoogle Scholar
  36. John, O.P., Srivastava, S.: The big five trait taxonomy: history, measurement, and theoretical perspectives. Handb. Personal. Theory Res. 2, 102–138 (1999)Google Scholar
  37. Kaminskas, M., Fernández-Tobías, I., Ricci, F., Cantador, I.: Knowledge-based identification of music suited for places of interest. Inf. Technol. Tour. 14(1), 73–95 (2014)CrossRefGoogle Scholar
  38. Kluver, D., Konstan, J.A.: Evaluating recommender behavior for new users. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 121–128. ACM (2014)Google Scholar
  39. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 426–434. ACM, New York (2008)Google Scholar
  40. Koren, Y., Bell, R.: Advances in collaborative filtering. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 145–186. Springer, Heidelberg (2011)Google Scholar
  41. Kosinski, M., Stillwell, D., Graepel, T: Private traits and attributes are predictable from digital records of human behavior. Proc. Natl Acad. Sci. USA 2–5 (2013)Google Scholar
  42. Kosinski, M., Stillwell, D., Kohli, P., Bachrach, Y., Graepel, T.: Personality and website choice. In: Proceedings of the 3rd Annual ACM Web Science Conference. ACM, New York (2012)Google Scholar
  43. Lehmann, J., Isele, R., Jakob, M., Jentzsch, A., Kontokostas, D., Mendes, P.N., Hellmann, S., Morsey, M., van Kleef, P., Auer, S., Bizer, C.: Dbpedia—a large-scale, multilingual knowledge base extracted from wikipedia. Semant. Web J. 6(2), 167–195 (2015)Google Scholar
  44. Lepri, B., Staiano, J., Shmueli, E., Pianesi, F., Pentland, A.: The role of personality in shaping social networks and mediating behavioral change. In: User Modeling, Adaptation, and Personalization—Special Issue on Personality in Personalized Systems. Springer (2016)Google Scholar
  45. Li, B., Yang, Q., Xue, X.: Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence, pp. 2052–2057 (2009)Google Scholar
  46. Li, Y., Hu, J., Zhai, C.X., Chen, Y.: Improving one-class collaborative filtering by incorporating rich user information. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 959–968. ACM, New York (2010)Google Scholar
  47. Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41(4, Part 2), 2065–2073 (2014)CrossRefGoogle Scholar
  48. Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  49. McNee, S.M., Lam, S.K., Konstan, J.A., Riedl, J.: Interfaces for eliciting new user preferences in recommender systems. In: Proceedings of the 9th International Conference on User Modeling. Springer (2003)Google Scholar
  50. Mello, C.E., Aufaure, M.-A., Zimbrao, G.: Active learning driven by rating impact analysis. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 341–344. ACM (2010)Google Scholar
  51. Nunes, M.A.S.N.: Recommender Systems Based on Personality Traits: Could Human Psychological Aspects Influence the Computer Decision-Making Process? VDM Verlag (2009)Google Scholar
  52. Nunes, M.A.S.N., Hu, R.: Personality-based recommender systems: an overview. In: Proceedings of the 6th ACM Conference on Recommender Systems, pp. 5–6 (2012)Google Scholar
  53. Odic, A., Tkalcic, M., Tasic, J.F., Kosirm, A.: Personality and social context: impact on emotion induction from movies. In: UMAP’13 Workshops (2013)Google Scholar
  54. Pan, R., Zhou, Y., Cao, B., Liu, N.N., Lukose, R., Scholz, M., Yang, Q.: One-class collaborative filtering. In: Proceedings of the 8th IEEE International Conference on Data Mining, pp. 502–511. IEEE, Washington, DC (2008)Google Scholar
  55. Pan, W., Xiang, E.W., Liu, N.N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence (2010)Google Scholar
  56. Park, S.-T., Chu, W.: Pairwise preference regression for cold-start recommendation. In: Proceedings of the 2009 ACM Conference on Recommender Systems, pp. 21–28 (2009)Google Scholar
  57. Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., Mcnee, S.M., Konstan, J.A., Riedl, J.: Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 2002 International Conference on Intelligent User Interfaces, pp. 127–134. ACM Press (2002)Google Scholar
  58. Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. SIGKDD Explor. 10(2), 90–100 (2008)CrossRefGoogle Scholar
  59. Rawlings, D., Ciancarelli, V.: Music preference and the five-factor model of the neo personality inventory. Psychol. Music 25(2), 120–132 (1997)CrossRefGoogle Scholar
  60. Rentfrow, P.J., Goldberg, L.R., Zilca, R.: Listening, watching, and reading: the structure and correlates of entertainment preferences. J. Personal. 79(2), 223–258 (2011)CrossRefGoogle Scholar
  61. Rentfrow, P.J., Gosling, S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Personal. Soc. Psychol. 84(6), 1236–1256 (2003)CrossRefGoogle Scholar
  62. Resnick, P., Varian, H.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  63. Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, New York (2011)CrossRefMATHGoogle Scholar
  64. Roshchina, A.: TWIN Personality-Based Recommender System. Institute of Technology Tallaght, Dublin (2012)Google Scholar
  65. Rubens, N., Kaplan, D., Sugiyama, M.: Active learning in recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 735–767. Springer, New York (2011)CrossRefGoogle Scholar
  66. Rubens, N., Sugiyama, M.: Influence-based collaborative active learning. In: Proceedings of the 2007 ACM Conference on Recommender Systems, pp. 145–148. ACM, New York (2007)Google Scholar
  67. Shapira, B., Rokach, L., Freilikhman, S.: Facebook single and cross domain data for recommendation systems. User Model. User Adapt. Interact. 23(2–3), 211–247 (2013)CrossRefGoogle Scholar
  68. Shi, Y., Larson, M., Hanjalic, A.: Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering. In: User Modeling, Adaption and Personalization, pp. 305–316. Springer, Berlin (2011)Google Scholar
  69. Son, L.H.: Dealing with the new user cold-start problem in recommender systems: a comparative review. Inf. Syst. (2014)Google Scholar
  70. Szomszor, M., Alani, H., Cantador, I., O’Hara, K., Shadbolt, N.: Semantic modelling of user interests based on cross-folksonomy analysis. In: Proceedings of the 7th International Semantic Web Conference, pp. 632–648 (2008)Google Scholar
  71. Tiroshi, A., Berkovsky, S., Kâafar, M.A., Chen, T., Kuflik, T.: Cross social networks interests predictions based on graph features. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 319–322 (2013)Google Scholar
  72. Tkalcic, M., Kunaver, M., Košir, A., Tasic, J.: Addressing the new user problem with a personality based user similarity measure. In: Proceedings of the 1st International Workshop on Decision Making and Recommendation Acceptance Issues in Recommender Systems, p. 106 (2011)Google Scholar
  73. Winoto, P., Tang, T.Y.: 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. N. Gener. Comput. 26(3), 209–225 (2008)CrossRefGoogle Scholar
  74. Yao, Y., Tong, H., Yan, G., Xu, F., Zhang, X., Szymanski, B.K., Lu, J.: Dual-regularized one-class collaborative filtering. In: Proceedings of the 23rd ACM International Conference on Information and Knowledge Management, pp. 759–768. ACM (2014)Google Scholar
  75. 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 the World Wide Web, pp. 22–32. ACM, New York (2005)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Ignacio Fernández-Tobías
    • 1
  • Matthias Braunhofer
    • 2
  • Mehdi Elahi
    • 2
  • Francesco Ricci
    • 2
  • Iván Cantador
    • 1
  1. 1.Universidad Autónoma de MadridMadridSpain
  2. 2.Free University of Bozen-BolzanoBozen-BolzanoItaly

Personalised recommendations