Enhancing personalized ranking quality through multidimensional modeling of inter-item competition
Document Type
Conference Proceeding
Date of Original Version
1-1-2010
Abstract
This paper presents MAPS - a personalized Multi-Attribute Probabilistic Selection framework - to estimate the probability of an item being a user's best choice and rank the items accordingly. The MAPS framework makes three original contributions in this paper. First, we capture the inter-attribute tradeoff by a visual angle model which maps multi-attribute items into points (stars) in a multidimensional space (sky). Second, we model the inter-item competition using the dominating areas of the stars. Third, we capture the user's personal preferences by a density function learned from his/her history. The MAPS framework carefully combines all three factors to estimate the probability of an item being a user's best choice, and produces a personalized ranking accordingly. We evaluate the accuracy of MAPS through extensive simulations. The results show that MAPS significantly outperforms existing multi-attribute ranking algorithms. © 2010 ICST.
Publication Title, e.g., Journal
Proceedings of the 6th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2010
Citation/Publisher Attribution
Feng, Qinyuan, Ling Liu, Yan Sun, Ting Yu, and Yafei Dai. "Enhancing personalized ranking quality through multidimensional modeling of inter-item competition." Proceedings of the 6th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2010 (2010). doi: 10.4108/icst.collaboratecom.2010.14.