S.-H. Min and I. Han, “Optimizing Collaborative Filtering Recommender Systems,” In: P. S. Szczepaniak and A. Niewiadomski, Eds., AWIC 2005, LNAI 3528, SpringerVerlag, Berlin Heidelberg, 2005, pp. 313-319.

has been cited by the following article:

• 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.