Game-theoretic rough sets for recommender systems

Recommender systems guide their users in decisions related to personal tastes and choices. The rough set theory can be considered as a useful tool for predicting recommendations in recommender systems. We examine two properties of recommendations with rough sets. The first property refers to accuracy or appropriateness of recommendations and the second property highlights the generality or coverage of recommendations. Making highly accurate recommendations for majority of the users is a major hindrance in achieving high quality performance for recommender systems. In the probabilistic rough set models, these two properties are controlled by thresholds (αβ). One of the research issues is to determine effective values of these thresholds based on the two considered properties. We apply the game-theoretic rough set (GTRS) model to obtain suitable values of these thresholds by implementing a game for determining a trade-off and balanced solution between accuracy and generality. Experimental results on movielen dataset suggest that the GTRS improves the two properties of recommendations leading to better overall performance compared to the Pawlak rough set model.

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