Vague preferences in recommender systems

Abstract Measuring similarity between preferences is a crucial problem for recommender systems. This task becomes significantly harder when preferences are incomplete or somehow vague. In this paper we show how to model vague preferences using IF-sets and then how to quantify similarity between preferences in a way that might be useful in collaborative filtering. We consider some comparison measures between IF-sets to find those possessing properties desirable in recommender systems. Then we construct some measures that might be useful in finding other customers somehow similar to our new user of a recommender system and in promoting those customers who have an extensive knowledge on many products not yet familiar to this new user. We also suggest how to combine the aforementioned methodology with some new entropy-based analytical and graphical tools to create recommendations and support customer’s decisions. The proposed graphical method for comparing possible recommendations due to several aspects enables to choose a recommendation that fits best to individual decision-making strategy of each user.