1 Fuzzy-Weighted Similarity Measures for Memory-Based Collaborative Recommender Systems

tive Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accordance of the Creative Commons Attribution License all Copyrights © 2014 are reserved for SCIRP and the owner of the intellectual property Mohammad Yahya H. Al-Shamri, Nagi H. Al-Ashwal. All Copyright © 2014 are guarded by law and by SCIRP as a guardian. Memory-based collaborative recommender system (CRS) computes the similarity between users based on their declared ratings. However, not all ratings are of the same importance to the user. The set of ratings each user weights highly differs from user to user according to his mood and taste. This is usually reflected in the user’s rating scale. Accordingly, many efforts have been done to introduce weights to the similarity measures of CRSs. This paper proposes fuzzy weightings for the most common similarity measures for memory-based CRSs. Fuzzy weighting can be considered as a learning mechanism for capturing the preferences of users for ratings. Com-paring with genetic algorithm learning, fuzzy weighting is fast, effective and does not require any more space. Moreover, fuzzy weightings based on the rating deviations from the user’s mean of ratings take into account the different rating scales of different users. The experimental results show that fuzzy weightings obviously improve the CRSs performance to a good extent