Abstract
Recommender systems (RSs) suffer from the cold-start or new user/item problem, i.e., the impossibility to provide a new user with accurate recommendations or to recommend new items. Active learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire her ratings and hence improve the output of the RS. In this paper, we propose a novel AL approach that exploits the user’s personality - using the Five Factor Model (FFM) - in order to identify the items that the user is requested to rate. We have evaluated our approach in a user study by integrating it into a mobile, context-aware RS that provides users with recommendations for places of interest (POIs). We show that the proposed AL approach significantly increases the number of ratings acquired from the user and the recommendation accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Short personality quiz - psych central. Based upon the Ten-Item Personality Inventory (TIPI) (February 2013)
Baltrunas, L., Ludwig, B., Peer, S., Ricci, F.: Context relevance assessment and exploitation in mobile recommender systems. Personal and Ubiquitous Computing 16(5), 507–526 (2012)
Costa, P., McCrae, R.: Toward a new generation of personality theories: Theoretical contexts for the five-factor model. In: The Five-Factor Model of Personality: Theoretical Perspectives, pp. 51–87 (1996)
Dunn, G., Wiersema, J., Ham, J., Aroyo, L.: Evaluating interface variants on personality acquisition for recommender systems. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 259–270. Springer, Heidelberg (2009)
Elahi, M., Repsys, V., Ricci, F.: Rating elicitation strategies for collaborative filtering. In: Huemer, C., Setzer, T. (eds.) EC-Web 2011. LNBIP, vol. 85, pp. 160–171. Springer, Heidelberg (2011)
Elahi, M., Ricci, F., Rubens, N.: Adapting to natural rating acquisition with combined active learning strategies. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds.) ISMIS 2012. LNCS, vol. 7661, pp. 254–263. Springer, Heidelberg (2012)
Elahi, M., Ricci, F., Rubens, N.: Active learning strategies for rating elicitation in collaborative filtering: a system-wide perspective. ACM Transactions on Intelligent Systems and Technology 5(1) (2014)
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 (2010)
Golbandi, N., Koren, Y., Lempel, R.: Adaptive bootstrapping of recommender systems using decision trees. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 595–604. ACM (2011)
Goldberg, L.R.: The development of markers for the big-five factor structure. Psychological Assessment 4(1), 26–42 (1992)
Gosling, S.D., Rentfrow, P.J., Swann, W.B.: A very brief measure of the big-five personality domains. Journal of Research in Personality 37, 504–528 (2003)
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, IUI 2009, pp. 367–372. ACM, New York (2009)
Hu, R., Pu, P.: Enhancing collaborative filtering systems with personality information. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 197–204. ACM, New York (2011)
John, O.P., Srivastava, S.: The big five trait taxonomy: History, measurement, and theoretical perspectives. In: Handbook of Personality: Theory and Research, vol. 2, pp. 102–138 (1999)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 2–5 (March 2013)
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, IUI 2002, pp. 127–134. ACM Press (2002)
Rashid, A.M., Karypis, G., Riedl, J.: Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explorations Newsletter 10(2), 90–100 (2008)
Rentfrow, P.J., Gosling, S.D., et al.: The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology 84(6), 1236–1256 (2003)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer (2011)
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 (2011)
Tkalcic, M., Kosir, A., Tasic, J.: The ldos-peraff-1 corpus of facial-expression video clips with affective, personality and user-interaction metadata. Journal on Multimodal User Interfaces 7(1-2), 143–155 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Elahi, M., Braunhofer, M., Ricci, F., Tkalcic, M. (2013). Personality-Based Active Learning for Collaborative Filtering Recommender Systems. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds) AI*IA 2013: Advances in Artificial Intelligence. AI*IA 2013. Lecture Notes in Computer Science(), vol 8249. Springer, Cham. https://doi.org/10.1007/978-3-319-03524-6_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-03524-6_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03523-9
Online ISBN: 978-3-319-03524-6
eBook Packages: Computer ScienceComputer Science (R0)