Enhancing collaborative filtering systems with personality information

RecSys'11 - Proceedings of the 5th ACM Conference on Recommender Systems

Volumn , Issue , 2011, Pages 197-204

  Hu, Rong ^a   Pu, Pearl ^a  

^a EPFL   (Switzerland)

Author keywords

cold start; collaborative filtering; personality; recommender system; user similarity

Indexed keywords

APPLICATION DOMAINS; CASCADE MECHANISM; COLD START; COLD START PROBLEMS; COLLABORATIVE FILTERING; COLLABORATIVE FILTERING SYSTEMS; EVALUATION METRICS; EXPERIMENTAL STUDIES; HISTORICAL DATA; LINEAR COMBINATIONS; PERSONALITY; PERSONALITY CHARACTERISTIC; RATING INFORMATION; RECOMMENDATION METHODS; SPARSE DATA SETS; SPARSITY PROBLEMS; USER PROBLEMS; USER SIMILARITY;

RECOMMENDER SYSTEMS; SENSITIVITY ANALYSIS;

RATING;

EID: 82555191361     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2043932.2043969     Document Type: Conference Paper

References (26)

1. Adomavicius, G. and Tuzhilin, A. 2005. Toward the Next Generation of Recommender Systems: A Survey of the Stateof- the-Art and Possible Extensions. IEEE Trans. on Knowl. and Data Eng. 17, 6, 734-749. (Pubitemid 40860454)
2. Ahn, H.J. 2008. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci. 178, 1, 37-51. (Pubitemid 47531785)
3. Billsus, D. and Pazzani, M.J. 1998. Learning Collaborative Information Filters. In Proceedings of the Fifteenth International Conference on Machine Learning (1998). Morgan Kaufmann Publishers Inc., 657311, 46-54.
4. Burger, J.M. 2010. Personality. Wadsworth Publishing, Belmont, CA.
5. Dunn, G., Wiersema, J., Ham, J. and Aroyo, L. 2009. Evaluating Interface Variants on Personality Acquisition for Recommender Systems.User Modeling, Adaptation, and Personalization, Houben, G.-J., McCalla, G., Pianesi, F. and Zancanaro, M., eds. Lecture Notes in Computer Science 5535, Springer Berlin / Heidelberg, 259-270.
6. Goldberg, K., Roeder, T., Gupta, D. and Perkins, C. 2001. Eigentaste: A Constant Time Collaborative Filtering Algorithm. Inf. Retr. 4, 2, 133-151. (Pubitemid 33636930)
7. Gonzalez, G., Rosa, J.L.d.l., Montaner, M. and Delfin, S. 2007. Embedding Emotional Context in Recommender Systems. In Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop (2007). IEEE Computer Society, 1547669, 845-852.
8. Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J. and Riedl, J. 1999. Combining collaborative filtering with personal agents for better recommendations. In Proc. Conf. Am. Assoc. Artificial Intelligence (AAAI-99) (Orlando, Florida, United States, 1999). AAAI, 315352, 439-446.
9. Gosling, S., Rentfrow, P. and Swann, W. 2003. A very brief measure of the Big-Five personality domains. Journal of Research in Personality. 37, 6, 504-528. (Pubitemid 37445626)
10. Herlocker, J.L., Konstan, J.A., Terveen, L.G. and Riedl, J.T. 2004. Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 1, 5-53.
11. Hu, R. and Pu, P. 2009. A comparative user study on rating vs. personality quiz based preference elicitation methods. In Proceedings of the 13th international conference on Intelligent user interfaces (Sanibel Island, Florida, USA, 2009). ACM, 1502702, 367-372.
12. Hu, R. and Pu, P. 2010. A Study on User Perception of Personality-Based Recommender Systems.User Modeling, Adaptation, and Personalization, De, B., Kobsa, A. and Chin, D., eds. 6075, Springer Berlin / Heidelberg, 291-302.
13. Huang, Z., Chen, H. and Zeng, D. 2004. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22, 1, 116-142.
14. Jung, C.G. 1971. Psychological Types. Princeton University Press, Princeton, N.J.
15. Kemp, A.E. 1996. The Music Temperament: Psychology and Personality of Musicians. Oxford University Press, New York.
16. Lekakos, G. and Giaglis, G.M. 2006. Improving the prediction accuracy of recommendation algorithms: Approaches anchored on human factors. Interact. Comput. 18, 3, 410-431.
17. Lin, C.-H. and McLeod, D. 2002. Exploiting and Learning Human Temperaments for Customized Information Recommendation. In Internet and Multimedia Systems and Applications (IMSA 2002) (Kaua'i, Hawaii, USA, 2002). IASTED/ACTA Press, 218-223.
18. Linden, G., Smith, B. and York, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing, IEEE. 7, 1, 76-80.
19. Minamikawa, A. and Yokoyama, H. 2011. Blog tells what kind of personality you have: egogram estimation from Japanese weblog. In Proceedings of the ACM 2011 conference on Computer supported cooperative work (Hangzhou, China, 2011). ACM, 1958856, 217-220.
20. Nguyen, A.-T., Denos, N. and Berrut, C. 2007. Improving new user recommendations with rule-based induction on cold user data. In Proceedings of the 2007 ACM conference on Recommender systems (Minneapolis, MN, USA, 2007). ACM, 1297251, 121-128.
21. Nunes, M.A.S.N. 2009. Recommender Systems based on Personality Traits: Could human psychological aspects influence the computer decision-making process? VDM Verlag, Berlin.
22. Park, S.-T., Pennock, D., Madani, O., Good, N. and DeCoste, D. 2006. Naïve filterbots for robust cold-start recommendations. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (Philadelphia, PA, USA, 2006). ACM, 1150490, 699-705.
23. Pazzani, M.J. 1999. A Framework for Collaborative, Content-Based and Demographic Filtering. Artif. Intell. Rev. 13, 5-6, 393-408.
24. Rentfrow, P.J. and Gosling, S.D. 2003. The do re mi's of everyday life: the structure and personality correlates of music preferences. J Pers Soc Psychol. 84, 6 (Jun), 1236-1256.
25. Resnick, P. and Varian, H.R. 1997. Recommender systems. Commun. ACM. 40, 3, 56-58. (Pubitemid 127441130)
26. Sarwar, B., Karypis, G., Konstan, J. and Riedl, J. 2000. Analysis of recommendation algorithms for e-commerce. In Proceedings of the 2nd ACM conference on Electronic commerce (Minneapolis, Minnesota, United States, 2000). ACM, 352887, 158-167.