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

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. Comparing 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.