Representation, similarity measures and aggregation methods using fuzzy sets for content-based recommender systems

Representation of features of items and user feedback, and reasoning about their relationships are major problems in recommender systems. This is because item features and user feedback are subjective, imprecise and vague. The paper presents a fuzzy set theoretic method (FTM) for recommender systems that handles the non-stochastic uncertainty induced from subjectivity, vagueness and imprecision in the data, and the domain knowledge and the task under consideration. The research further advances the application of fuzzy modeling for content-based recommender systems initially presented by Ronald Yager. The paper defines a representation method, similarity measures and aggregation methods as well as empirically evaluates the methods’ performance through simulation using a benchmark movie data. FTM consist of a representation method for items’ features and user feedback using fuzzy sets, and a content-based algorithm based on various fuzzy set theoretic similarity measures (the fuzzy set extensions of the Jaccard index, cosine, proximity or correlation similarity measures), and aggregation methods for computing recommendation confidence scores (the maximum–minimum or Weighted-sum fuzzy set theoretic aggregation methods). Compared to the baseline crisp set based method (CSM) presented, the empirical evaluation of the FTM using the movie data and simulation shows an improvement in precision without loss of recall. Moreover, the paper provides a guideline for recommender systems designers that will help in choosing from a combination of one of the fuzzy set theoretic aggregation methods and similarity measures.