Affiliations: Department of Computer Science, University of Delhi, Delhi, India
Correspondence:
[*]
Corresponding author. Pooja Vashisth, Postal Address: L-37, Street No. 22, New Mahavir Nagar, New Delhi 110018,
India. Tel.: +91 9971696984; E-mail: [email protected].
Abstract: Recommender Systems (RSs) are largely used nowadays to generate interest items or products for web users of diverse nature. Therefore, this work focuses on using fuzzy logic to accommodate diversity and uncertainty in user choices and interest. This would help in generating better recommendations with different tastes that correspond to different interest choices of the user. In this paper, a fuzzy hybrid multi-agent recommender system is designed and developed. The novelty of our approach is the use of interval type-2 fuzzy sets to create user models capable of capturing the inherent ambiguity of human behavior related to diverse users’ tastes. In the due course, we also extended an existing, well known hybrid recommendation method, by integrating the proposed fuzzy approach into the recommendation process. As a result, a new RS approach was developed, which was capable of improving the prediction accuracy of system and at the same time reducing errors by being able to extract more information from the available dataset. Experimental study and analysis was conducted using two case studies namely book purchase and shopping women apparels. As a result, the proposed recommendation approach was found to perform considerably well as compared to its counterparts, even under data sparsity conditions.
