Rong Hu
Pearl Pu
ABSTRACT
Collaborative filtering (CF), one of the most successful recommendation approaches, continues to attract interest in both academia and industry. However, one key issue limiting the success of collaborative filtering in certain application domains is the cold-start problem, a situation where historical data is too sparse (known as the sparsity problem), new users have not rated enough items (known as the new user problem), or both. In this paper, we aim at addressing the cold-start problem by incorporating human personality into the collaborative filtering framework. We propose three approaches: the first is a recommendation method based on users' personality information alone; the second is based on a linear combination of both personality and rating information; and the third uses a cascade mechanism to leverage both resources. To evaluate their effectiveness, we have conducted an experimental study comparing the proposed approaches with the traditional rating-based CF in two cold-start scenarios: sparse data sets and new users. Our results show that the proposed CF variations, which consider personality characteristics, can significantly improve the performance of the traditional rating-based CF in terms of the evaluation metrics MAE and ROC sensitivity.
- Adomavicius, G. and Tuzhilin, A. 2005. Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on Knowl. and Data Eng. 17, 6, 734--749. Google Scholar
Digital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Burger, J.M. 2010. Personality. Wadsworth Publishing, Belmont, CA.Google Scholar
- 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. Google ScholarDigital Library
- Goldberg, K., Roeder, T., Gupta, D. and Perkins, C. 2001. Eigentaste: A Constant Time Collaborative Filtering Algorithm. Inf. Retr. 4, 2, 133--151. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- Jung, C.G. 1971. Psychological Types. Princeton University Press, Princeton, N.J.Google Scholar
- Kemp, A.E. 1996. The Music Temperament: Psychology and Personality of Musicians. Oxford University Press, New York.Google Scholar
- 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. Google ScholarDigital Library
- 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.Google Scholar
- Linden, G., Smith, B. and York, J. 2003. Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing, IEEE. 7, 1, 76--80. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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. Google ScholarDigital Library
- 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.Google Scholar
- 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. Google ScholarDigital Library
- Pazzani, M.J. 1999. A Framework for Collaborative, Content-Based and Demographic Filtering. Artif. Intell. Rev. 13, 5-6, 393--408. Google ScholarDigital Library
- 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.Google ScholarCross Ref
- Resnick, P. and Varian, H.R. 1997. Recommender systems. Commun. ACM. 40, 3, 56--58. Google ScholarDigital Library
- 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. Google ScholarDigital Library
Index Terms
- Enhancing collaborative filtering systems with personality information
Recommendations
Merging trust in collaborative filtering to alleviate data sparsity and cold start
Providing high quality recommendations is important for e-commerce systems to assist users in making effective selection decisions from a plethora of choices. Collaborative filtering is a widely accepted technique to generate recommendations based on ...
Analyzing weighting schemes in collaborative filtering: cold start, post cold start and power users
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied ComputingCollaborative filtering recommender systems provide their users with relevant items based on information from other similar users. Popular collaborative filtering approaches such as Pearson correlation coefficient and cosine similarity, compute the ...
An Improved Collaborative Filtering Model Considering Item Similarity
ISCC-C '13: Proceedings of the 2013 International Conference on Information Science and Cloud Computing CompanionBecause of its simplicity and effectiveness, collaborative filtering (CF) became one of the most successful recommendation algorithms. User-based CF is one classic method of CF algorithms. In order to solve the problem that common rating items are often ...
Comments