Advertisement

User Modeling and User-Adapted Interaction

, Volume 28, Issue 3, pp 237–276 | Cite as

Personalizing recommendation diversity based on user personality

  • Wen Wu
  • Li Chen
  • Yu Zhao
Article
  • 186 Downloads

Abstract

In recent years, diversity has attracted increasing attention in the field of recommender systems because of its ability of catching users’ various interests by providing a set of dissimilar items. There are few endeavors to personalize the recommendation diversity being tailored to individual users’ diversity needs. However, they mainly depend on users’ behavior history such as ratings to customize diversity, which has two limitations: (1) They neglect taking into account a user’s needs that are inherently caused by some personal factors such as personality; (2) they fail to work well for new users who have little behavior history. In order to address these issues, this paper proposes a generalized, dynamic personality-based greedy re-ranking approach to generating the recommendation list. On one hand, personality is used to estimate each user’s diversity preference. On the other hand, personality is leveraged to alleviate the cold-start problem of collaborative filtering recommendations. The experimental results demonstrate that our approach significantly outperforms related methods (including both non-diversity-oriented and diversity-oriented methods) in terms of metrics measuring recommendation accuracy and personalized diversity degree, especially in the cold-start setting.

Keywords

Recommender system Diversity Personality traits User survey Greedy re-ranking 

Notes

Acknowledgements

We thank all participants who took part in our user survey. We also thank reviewers for their suggestions and comments. In addition, we thank Hong Kong Research Grants Council (RGC) for sponsoring the research work (under Project RGC/HKBU12200415).

References

  1. Adomavicius, G., Kwon, Y.: Toward more diverse recommendations: item re-ranking methods for recommender systems. In: Workshop on Information Technologies and Systems (WITS 2009), pp. 417–440. Citeseer (2009)Google Scholar
  2. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  3. Ajzen, I.: Attitudes, Personality, and Behavior. McGraw-Hill Education, London (2005)Google Scholar
  4. Armstrong, R.A.: When to use the bonferroni correction. Ophthalmic Physiol. Opt. 34(5), 502–508 (2014)CrossRefGoogle Scholar
  5. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002)CrossRefzbMATHGoogle Scholar
  6. Bradley, K., Smyth, B.: Improving recommendation diversity. In: Proceedings of the 12th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2001), pp. 85–94 (2001)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 (UAI 1998), pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  8. Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 1998), pp. 335–336. ACM (1998)Google Scholar
  9. Celli, F., Pianesi, F., Stillwell, D., Kosinski, M.: Workshop on computational personality recognition (shared task). In: Proceedings of the Workshop on Computational Personality Recognition (2013)Google Scholar
  10. Chen, D., Plemmons, R.J.: Nonnegativity constraints in numerical analysis. Birth Numer. Anal. 10, 109–140 (2009)CrossRefzbMATHGoogle Scholar
  11. Chen, L., Pu, P.: Preference-based organization interfaces: aiding user critiques in recommender systems. User Model. 2007, 77–86 (2007)Google Scholar
  12. Chen, L., Wu, W., He, L.: How personality influences users’ needs for recommendation diversity? In: Proceedings of the 31st ACM Conference on Human Factors in Computing Systems (CHI 2013 Extended Abstracts), pp. 829–834. ACM (2013)Google Scholar
  13. Chen, L., Wu, W., He, L.: Personality and recommendation diversity. In: Emotions and Personality in Personalized Services, vol. 3, pp. pp–201. Springer International Publishing (2016)Google Scholar
  14. Clarke, C.L., Kolla, M., Cormack, G.V., Vechtomova, O., Ashkan, A., Büttcher, S., MacKinnon, I.: Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2008), pp. 659–666. ACM (2008)Google Scholar
  15. Cohen, P., Cohen, J., Aiken, L.S., West, S.G.: The problem of units and the circumstance for pomp. Multivar. Behav. Res. 34(3), 315–346 (1999)CrossRefGoogle Scholar
  16. Cronbach, L.J.: Theory of generalizability for scores and profiles. The Dependability of Behavioral Measurements pp. 161–188 (1972)Google Scholar
  17. De Vries, L., Gensler, S., Leeflang, P.S.: Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing. J. Interact. Mark. 26(2), 83–91 (2012)CrossRefGoogle Scholar
  18. Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Recommender Systems Handbook, pp. 107–144. Springer, Berlin (2011)Google Scholar
  19. Di Noia, T., Ostuni, V.C., Rosati, J., Tomeo, P., Di Sciascio, E.: An analysis of users’ propensity toward diversity in recommendations. In: Proceedings of the 8th ACM Conference on Recommender Systems (RecSys 2014), pp. 285–288. ACM (2014)Google Scholar
  20. Digman, J.M.: Personality structure: emergence of the five-factor model. Annu. Rev. Psychol. 41(1), 417–440 (1990)CrossRefGoogle Scholar
  21. Eskandanian, F., Mobasher, B., Burke, R.: A clustering approach for personalizing diversity in collaborative recommender systems. In: Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (UMAP 2017), pp. 280–284. ACM (2017)Google Scholar
  22. Ge, M., Delgado-Battenfeld, C., Jannach, D.: Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the 4th ACM Conference on Recommender Systems (RecSys 2010), pp. 257–260. ACM (2010)Google Scholar
  23. Helson, R., Soto, C.J.: Up and down in middle age: monotonic and nonmonotonic changes in roles, status, and personality. J. Pers. Soc. Psychol. 89(2), 194 (2005)CrossRefGoogle Scholar
  24. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  25. Hofmann, T.: Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. 22(1), 89–115 (2004)CrossRefGoogle Scholar
  26. Hu, R., Pu, P.: Acceptance issues of personality-based recommender systems. In: Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys 2009), pp. 221–224. ACM (2009)Google Scholar
  27. Hu, R., Pu, P.: A study on user perception of personality-based recommender systems. User Modeling, Adaptation, and Personalization (UMAP 2010), pp. 291–302 (2010)Google Scholar
  28. Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Proceedings of the 5th ACM Conference on Recommender Systems (RecSys 2011), pp. 197–204. ACM (2011)Google Scholar
  29. Hu, R., Pu, P.: Helping users perceive recommendation diversity. In: DiveRS@ RecSys, pp. 43–50 (2011)Google Scholar
  30. Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Proceedings of the 8th International Conference on Data Mining (ICDM 2008), pp. 263–272. IEEE (2008)Google Scholar
  31. Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)CrossRefGoogle Scholar
  32. John, O.P., Srivastava, S.: The big five trait taxonomy: history, measurement, and theoretical perspectives. Handb. Pers. Theory Res. 2(1999), 102–138 (1999)Google Scholar
  33. Kaiseler, M., Polman, R.C., Nicholls, A.R.: Effects of the big five personality dimensions on appraisal coping, and coping effectiveness in sport. Eur. J. Sport Sci. 12(1), 62–72 (2012)CrossRefGoogle Scholar
  34. Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans. Interact. Intell. Syst. 7(1), 2 (2016)CrossRefGoogle Scholar
  35. Karumur, R.P., Nguyen, T.T., Konstan, J.A.: Personality, user preferences and behavior in recommender systems. Inf. Syst. Front. 6, 1–25 (2017)Google Scholar
  36. Kaufman, L., Rousseeuw, P.: Clustering by Means of Medoids. North-Holland, Amsterdam (1987)Google Scholar
  37. Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Model. User-Adap. Interact. 22(4–5), 441–504 (2012)CrossRefGoogle Scholar
  38. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  39. Lawson, C.L., Hanson, R.J.: Solving Least Squares Problems. SIAM, Philadelphia (1995)CrossRefzbMATHGoogle Scholar
  40. McCrae, R.R., Terracciano, A.: Personality profiles of cultures: aggregate personality traits. J. Pers. Soc. Psychol. 89(3), 407 (2005)CrossRefGoogle Scholar
  41. McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: Proceedings of the 24th ACM Conference on Human Factors in Computing Systems (CHI 2006 Extended Abstracts), pp. 1097–1101. ACM (2006)Google Scholar
  42. Mourão, F., Fonseca, C., Araujo, C.S., Meira Jr, W.: The oblivion problem: exploiting forgotten items to improve recommendation diversity. In: DiveRS@ RecSys, pp. 27–34 (2011)Google Scholar
  43. Nadkarni, A., Hofmann, S.G.: Why do people use facebook? Pers. Individ. Differ. 52(3), 243–249 (2012)CrossRefGoogle Scholar
  44. Nguyen, T.T., Hui, P.M., Harper, F.M., Terveen, L., Konstan, J.A.: Exploring the filter bubble: the effect of using recommender systems on content diversity. In: Proceedings of the 23rd International Conference on World Wide Web (WWW 2014), pp. 677–686. ACM (2014)Google Scholar
  45. Nunes, M.A.S., Hu, R.: Personality-based recommender systems: an overview. In: Proceedings of the 6th ACM Conference on Recommender Systems (RecSys 2012), pp. 5–6. ACM (2012)Google Scholar
  46. Nunnally, J.C., Bernstein, I.H., Berge, J.M.T.: Psychometric Theory, vol. 226. McGraw-Hill, New York (1967)Google Scholar
  47. Perrett, D., Schaffer, J., Piccone, A., Roozeboom, M., et al.: Bonferroni adjustments in tests for regression coefficients. Mult. Linear Regres. Viewp. 32, 1–6 (2006)Google Scholar
  48. Powers, D.M.: Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)Google Scholar
  49. Qian, G., Sural, S., Gu, Y., Pramanik, S.: Similarity between euclidean and cosine angle distance for nearest neighbor queries. In: Proceedings of the 19th ACM Symposium on Applied Computing (SAC 2004), pp. 1232–1237. ACM (2004)Google Scholar
  50. Rentfrow, P.J., Gosling, S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84(6), 1236 (2003)CrossRefGoogle Scholar
  51. Rényi, A., et al.: On measures of entropy and information. In: Proceedings of the 4th Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics. The Regents of the University of California (1961)Google Scholar
  52. Roberts, B.W.: Back to the future: personality and assessment and personality development. J. Res. Pers. 43(2), 137–145 (2009)CrossRefGoogle Scholar
  53. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web (WWW 2001), pp. 285–295. ACM (2001)Google Scholar
  54. Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: The Aaptive Web, pp. 291–324. Springer, Berlin (2007)Google Scholar
  55. Seber, G.A., Lee, A.J.: Linear Regression Analysis, vol. 329. Wiley, New York (2012)zbMATHGoogle Scholar
  56. Sha, C., Wu, X., Niu, J.: A framework for recommending relevant and diverse items. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), pp. 3868–3874 (2016)Google Scholar
  57. Shani, G., Gunawardana, A.: Evaluating recommendation systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 257–297. Springer, Boston (2011)CrossRefGoogle Scholar
  58. Shi, Y., Larson, M., Hanjalic, A.: List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the 4th ACM Conference on Recommender Systems (RecSys 2010), pp. 269–272. ACM (2010)Google Scholar
  59. Shi, Y., Zhao, X., Wang, J., Larson, M., Hanjalic, A.: Adaptive diversification of recommendation results via latent factor portfolio. In: Proceedings of the 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2012), pp. 175–184. ACM (2012)Google Scholar
  60. Smyth, B., McClave, P.: Similarity vs. diversity. In: Aha, D.W., Watson, I. (eds.) Case-Based Reasoning Research and Development, pp. 347–361. Springer, Berlin (2001)CrossRefGoogle Scholar
  61. Srivastava, S., John, O.P., Gosling, S.D., Potter, J.: Development of personality in early and middle adulthood: set like plaster or persistent change? J. Pers. Soc. Psychol. 84(5), 1041 (2003)CrossRefGoogle Scholar
  62. Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)CrossRefGoogle Scholar
  63. Thackeray, R., Neiger, B.L., Smith, A.K., Van Wagenen, S.B.: Adoption and use of social media among public health departments. BMC Pub. Health 12(1), 242 (2012)CrossRefGoogle Scholar
  64. 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 (UMAP 2013), pp. 190–202. Springer, Berlin (2013)Google Scholar
  65. 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
  66. Tkalcic, M., Quercia, D., Graf, S.: Preface to the special issue on personality in personalized systems. User Model. User-Adap. Interact. 26(2–3), 103 (2016)CrossRefGoogle Scholar
  67. Tobias, I.F., Braunhofer, M., Elahi, M., Ricci, F., Ivan, C.: Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model. User-Adapt. Interact. 26, 221–255 (2016)CrossRefGoogle Scholar
  68. Vargas, S., Castells, P.: Exploiting the diversity of user preferences for recommendation. In: Proceedings of the 10th Conference on Open Research Areas in Information Retrieval (OAIR 2013), pp. 129–136. LE CENTRE DE HAUTES ETUDES INTERNATIONALES D’INFORMATIQUE DOCUMENTAIRE (2013)Google Scholar
  69. Wang, J., Zhu, J.: Portfolio theory of information retrieval. In: Proceedings of the 32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2009), pp. 115–122. ACM (2009)Google Scholar
  70. Willemsen, M.C., Graus, M.P., Knijnenburg, B.P.: Understanding the role of latent feature diversification on choice difficulty and satisfaction. User Model. User-Adap. Interact. 26(4), 347–389 (2016)CrossRefGoogle Scholar
  71. Wood, D., Wortman, J.: Trait means and desirabilities as artifactual and real sources of differential stability of personality traits. J. Pers. 80(3), 665–701 (2012)CrossRefGoogle Scholar
  72. Wu, W., Chen, L.: Implicit acquisition of user personality for augmenting movie recommendations. In: International Conference on User Modeling, Adaptation, and Personalization (UMAP 2015), pp. 302–314. Springer, Berlin (2015)Google Scholar
  73. 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 (HT 2013), pp. 225–229. ACM (2013)Google Scholar
  74. Wu, W., He, L., Yang, J.: Evaluating recommender systems. In: Proceedings of the 7th International Conference on Digital Information Management (ICDIM 2012), pp. 56–61. IEEE (2012)Google Scholar
  75. Zeng, W., Shang, M.S., Zhang, Q.M., Lü, L., Zhou, T.: Can dissimilar users contribute to accuracy and diversity of personalized recommendation? Int. J. Mod. Phys. C 21(10), 1217–1227 (2010)CrossRefGoogle Scholar
  76. Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: Proceedings of the 2nd ACM Conference on Recommender Systems (RecSys 2008), pp. 123–130. ACM (2008)Google Scholar
  77. 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 World Wide Web (WWW 2005), pp. 22–32. ACM (2005)Google Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceHong Kong Baptist UniversityKowloon Tong, Hong KongChina
  2. 2.Douban Inc.BeijingChina

Personalised recommendations