Context Dependent Preference Acquisition with Personality-Based Active Learning in Mobile Recommender Systems
Nowadays, Recommender Systems (RSs) play a key role in many businesses. They provide consumers with relevant recommendations, e.g., Places of Interest (POIs) to a tourist, based on user preference data, mainly in the form of ratings for items. The accuracy of recommendations largely depends on the quality and quantity of the ratings (preferences) provided by the users. However, users often tend to rate no or only few items, causing low accuracy of the recommendation. Active Learning (AL) addresses this problem by actively selecting items to be presented to the user in order to acquire a larger number of high-quality ratings (preferences), and hence, improve the recommendation accuracy. In this paper, we propose a personalized active learning approach that leverages user’s personality data to get more and better in-context ratings. We have designed a novel human computer interaction and assessed our proposed approach in a live user study - which is not common in active learning research. The main result is that the system is able to collect better ratings and provide more relevant recommendations compared to a variant that is using a state of the art approach to preference acquisition.
KeywordsRecommender Systems Collaborative Filtering Personalized Active Learning Cold start Mobile
Unable to display preview. Download preview PDF.
- 1.Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer (2011)Google Scholar
- 2.Adomavicius, G., Mobasher, B., Ricci, F., Tuzhilin, A.: Context-aware recommender systems. AI Magazine 32(3), 67–80 (2011)Google Scholar
- 3.Carenini, G., Smith, J., Poole, D.: Towards more conversational and collaborative recommender systems. In: Proceedings of the 2003 International Conference on Intelligent User Interfaces, Miami, FL, USA, January 12-15, pp. 12–18 (2003)Google Scholar
- 5.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)Google Scholar
- 6.Elahi, M., Ricci, F., Rubens, N.: Active learning strategies for rating elicitation in collaborative filtering: A system-wide perspective. ACM Trans. Intell. Syst. Technol. 5(1), 13:1–13:33 (2014)Google Scholar
- 9.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)Google Scholar
- 12.Braunhofer, M., Elahi, M., Ricci, F., Schievenin, T.: Context-aware points of interest suggestion with dynamic weather data management. In: 21st Conference on Information and Communication Technologies in Tourism (ENTER). Springer (2014)Google Scholar
- 16.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, WSDM 2011, pp. 595–604. ACM, New York (2011)Google Scholar