@inproceedings{787ccd5fd5b74177b1702af01a7e388e,
title = "Enhancing personalized ranking quality through multidimensional modeling of inter-item competition",
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.",
author = "Qinyuan Feng and Ling Liu and Yan Sun and Ting Yu and Yafei Dai",
year = "2010",
doi = "10.4108/icst.collaboratecom.2010.14",
language = "English",
isbn = "9780984589326",
series = "Proceedings of the 6th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2010",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of the 6th International Conference on Collaborative Computing",
address = "United States",
}