TITLE:
Fuzzy-Weighted Similarity Measures for Memory-Based Collaborative Recommender Systems
AUTHORS:
Mohammad Yahya H. Al-Shamri, Nagi H. Al-Ashwal
KEYWORDS:
Collaborative Recommender Systems; Pearson Correlation Coefficient; Cosine Similarity Measure; Mean Difference Weights Similarity Measure; Fuzzy Weighting
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.6 No.1,
January
27,
2014
ABSTRACT: 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.