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

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.

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

“…The approach is used to retrieve relevant documents. In the other research, Al-shamri and Al-Ashwal presented fuzzy weightings of popular similarity measures for memory-based collaborative recommend systems [18].…”

“…However, to deal with the high level of uncertainty of the processed signals, numerous similarity measures can be used to compute similarity like the cosine similarity, Euclidean distance, Pearson correlation coefficient. Moreover, a fuzzyweighted combination of scores generated from different similarity measures could comparatively achieve better retrieval results than the use of a single similarity measure [12,18].…”

“…Recent works to urban traffic flow prediction [15] and to lane-changes prediction [16] have been proposed with success. Furthermore, the successful use of fuzzy-based similarity measure in pattern recognition [17], in retrieval systems [12], and in recommendation systems [18] leads us to study its ability to complete missing values in uncorrelated multivariate time series. Wang et al [19] proposed using information granules and fuzzy clustering for time series long-term forecasting.…”

Effect of Collaborative Recommender System Parameters: Common Set Cardinality and the Similarity Measure

“…Many papers have discussed and proposed many similarity measures, but they fixed the lowest number of the common items in advance and examined their proposals based on that predefined number [3][4][5][6][7][8][9]. For example, Al-Shamri [3] examined traditional approaches and proposed a power coefficient as a similarity measure but he assumed the common set size is greater than or equal to five.…”

“…Actually, many of the existing similarity measures for collaborative recommender systems rely on the overlapping between users. Nevertheless, the size of this overlapping is not explored in detail where most of the previous work studied similarity measures based on a predefined number of common items [3][4][5][6][7][8].…”