Fuzzy Sets and Systems Manuscript Draft Manuscript Number: FSS-D-07-00507 Title: Representation, Similarity Measures and Aggregation Methods Using Fuzzy Theory for Content-based Recommender Systems

Representation, Similarity Measures and Aggregation Methods Using Fuzzy Theory for Contentbased Recommender Systems Azene Zenebe (corresponding author), Information Systems Department, Bowie State University, 14000 Jericho Park Rd., Bowie, MD 20715-9465, USA. E-mail: azenebe@bowiestate.edu. Telephone: (001)(301)-860-3641. Fax: (001)(301)-860-3644 Anthony F. Norcio, Information Systems Department, University of Maryland at Baltimore County, USA’ norcio@umbc.edu Abstract—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 theoretic method (FTM) for recommender systems that handles the nonstochastic 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 is consisting of representation method for items’ features and user feedback using fuzzy set, and a content-based algorithm based on various fuzzy theoretic similarity measures (the fuzzy theoretic 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 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 technique 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 theoretic aggregation methods and similarity measures.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 theoretic method (FTM) for recommender systems that handles the nonstochastic 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 is consisting of representation method for items’ features and user feedback using fuzzy set, and a content-based algorithm based on various fuzzy theoretic similarity measures (the fuzzy theoretic 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 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 technique 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 theoretic aggregation methods and similarity measures.