On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy

2019 ◽  
pp. 189-200
Author(s):  
Yuchi Kanzawa
Keyword(s):  
Collaborative Filtering ◽  
Tsallis Entropy ◽  
Spherical Data ◽  
Possibilistic Clustering

scholarly journals Collaborative filtering with q-divergence-based fuzzy clustering for spherical data

2021 ◽  
Author(s):  
Yuchi Kanzawa ◽  
Tadafumi Kondo
Keyword(s):  
Cluster Analysis ◽  
Collaborative Filtering ◽  
Fuzzy Clustering ◽  
Recommendation System ◽  
Multivariate Data ◽  
Receiver Operating Curve ◽  
Spherical Data ◽  
The People ◽  
Target User ◽  
Recommendation Accuracy

AbstractAlthough recommendation systems are the most powerful tool to help people choose items, a higher recommendation accuracy is required to satisfy the needs of the people. Motivated by this requirement, this study proposes a novel collaborative filtering (CF) algorithm, which is the underlying technology of a recommendation system. It filters items for a target user based on the reactions of similar users. Cluster analysis helps detect similar users by grouping a set of users such that users in the same group are more similar to each other than to those in other groups. However, in most representative CF algorithms such as GroupLens algorithm, users are considered as spherical data, and as categorical multivariate data in the clustering phase of a previous study. This study overcomes this logic gap by proposing a novel CF method using fuzzy clustering for spherical data based on q-divergence as both the clustering phase and the GroupLens algorithm consistently deal with users as spherical data. Experiments were conducted on six real datasets—BookCrossing, Epinions, Jester, LibimSeTi, MovieLens, and SUSHI, to compare the performance of the proposed method with GroupLens and the method using fuzzy clustering for categorical multivariate data based on q-divergence, which are conventional methods, where the performance is measured by the area under the receiver operating curve. The results of the experiments indicate that the proposed algorithm outperforms the others in terms of recommendation accuracy.

Collaborative Filtering for Creation of Recommendations on Base of Order Data

2018 ◽  
Vol 16 (2) ◽  
pp. 62-69
Author(s):  
A. A. Knyazeva ◽  
◽  
O. S. Kolobov ◽  
I. Yu. Turchanovsky ◽  
A. M. Fedotov ◽  
...  
Keyword(s):  
Collaborative Filtering
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