Fuzzy Modeling for Item Recommender Systems Or A Fuzzy Theoretic Method for Recommender Systems

Representation of features of items and user feedbacks that are subjective, incomplete, imprecise and vague, and reasoning about their relationships are major problems in recommender systems. The paper presents a Fuzzy 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 task under consideration. FTM further advances methods of fuzzy modeling in recommender systems as well as empirically evaluates the methods’ performance through simulations using a benchmark movie data. FTM is comprised of representation method for items’ feature and user feedbacks on these items, and a content-based algorithm based on various fuzzy theoretic similarity measures such as the fuzzy theoretic extensions of the Jaccard Index, Cosine, Proximity or Correlation similarity measures, and recommendation strategies such as the maximum-minimum or weighted-sum fuzzy theoretic recommendation strategies. Compared to the baseline Crisp Set based method (CSM), simulation runs of the FTM using the movie data show an improvement in precision with comparable recall and F1-measure. The improvement in the performance of FTM is attributed to the use of fuzzy modeling. Also, lower model size and recommendation size produce satisfactory recommendation accuracy. Moreover, depending on a task, a guideline for recommender systems designers that will help them choose a combination of one of the fuzzy theoretic recommendation strategies and the fuzzy theoretic similarity measures is provided. From the results we can conclude that modeling uncertainty using fuzzy set and logic improves the performance of content-based recommender systems.