Contribution to Collaborative Filtering Based on Soft Computing to Enhance Recommender System for e-Commerce Saad M. Darwish 1 *, Magda M. Madbouly 2 , Eman Abd-El Reheem 3 1, 2, 3 Institute of Graduate Studies and Research, Alexandria University, 163Horreya Avenue, El-Shatby, 21526 P.O. Box 832, Alexandria, Egypt. * Corresponding author. Tel.: +201222632369; email: Saad.darwish@alex-igsr.edu.eg Manuscript submitted June 10, 2014; accepted August 28, 2014. doi: 10.7763/ijeeee.2014.V4.341 Abstract: Recommender Systems (RSs) are used by an ever-increasing number of e-commerce sites to recommend items of interest to the users based on their preferences. Collaborative filtering is one of the most regularly used techniques in RSs that help the users to catch the items of interest from a massive numbers of available items. This technique is based on the idea that a set of like-mind users can help each other to find valuable information. The major challenge in recommender systems is that the user ratings or grades are very often uncertain or vague because it is based on user’s tastes, opinions, and perceptions. Fuzzy sets appear to be a proper paradigm to handle the uncertainty and fuzziness of human decision making activities and to successfully model the normal sophistication of human behavior. Because of these motives, this paper adopts type-2 fuzzy linguistic approach to efficiently describe the user ratings and weights to precisely rank the relevant items to a user. The proposed method permits users to express their ratings in qualitative form, converts such preferences to their corresponding quantitative form using the concept of type-2 fuzzy logic, maps the values that represent the preferences with the retrieved items from the database, and finally recommends products that best satisfy the consumer’s likings. Empirical evaluations show that the proposed technique is feasible and effective. Key words: Collaborative filtering, multicriteria decision making, type-2 fuzzy linguistic, recommender systems. 1. Introduction Now, with the rapid growth of the Internet, the explosive evolution and variety of information available on the Web and the accessibility of a large amount of products for sale in e-commerce sites, have led to information overload problem; consumers have to spend more time glancing the Net in order to find the information needed, and it has also become very difficult for customers to attain the most suitable choices from the massive variety of products leading them to make poor decisions. So, developers found a solution in Recommender Systems (RS). RSs have proven in recent years to be a valuable means for coping with the information overload problem and to provide recommendations of products likely to interest a user [1]. This technique is generally used in e-commerce to advise items that a customer is assembly going to buy [1]. For another point of view, RS is a platform that can be used to diminish the searching cost of customers and improve customer’s loyalty . International Journal of e-Education, e-Business, e-Management and e-Learning 257 Volume 4, Number 4, August 2014