Nonadditive similarity-based single-layer perceptron for multi-criteria collaborative filtering
The main aim of the popular collaborative filtering approaches for recommender systems is to recommend items that users with similar preferences have liked in the past. Although single-criterion recommender systems have been successfully used in several applications, multi-criteria rating systems that allow users to specify ratings for various content attributes for individual items are gaining in importance. To measure the overall similarity between any two users for multi-criteria collaborative filtering, the indifference relation in outranking relation theory, which can justify discrimination between any two patterns, is suitable for multi-criteria decision making (MCDM). However, nonadditive indifference indices that address interactions among criteria should be taken into account. This paper proposes a novel similarity-based perceptron using nonadditive indifference indices to estimate an overall rating that a user would give to a specific item. The applicability of the proposed model to recommendation of initiators on a group-buying website was examined. Experimental results demonstrate that the proposed model performs well in terms of generalization ability compared to other multi-criteria collaborative filtering approaches.
perceptron collaborative filtering indifference relation mcdm fuzzy integral