Abstract
Recommender systems are emerging as an important business tool in E-commerce. The recommendations in these systems typically rely on some sort of intelligent mechanisms that analyze previous user trends and ratings to make personalized recommendations. In this article, we examine the application of game-theoretic rough set (GTRS) model as an alternative intelligent component for recommender systems. The role of GTRS is examined by considering two important properties of recommendations. The first property is the accuracy of recommendations and the second property is the generality or support of recommendations. It is argued that making highly accurate recommendations for majority of the users is a major hindrance and difficulty for improving the performance of recommender systems. The GTRS meets this challenge by examining a tradeoff solution between the properties of accuracy and generality. Experimental results on movielen dataset suggest that the GTRS improves the two properties of recommendations compared to the standard Pawlak rough set model.
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Azam, N., Yao, J. (2014). Application of Game-Theoretic Rough Sets in Recommender Systems. In: Murty, M.N., He, X., Chillarige, R.R., Weng, P. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2014. Lecture Notes in Computer Science(), vol 8875. Springer, Cham. https://doi.org/10.1007/978-3-319-13365-2_9
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DOI: https://doi.org/10.1007/978-3-319-13365-2_9
Publisher Name: Springer, Cham
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