Employing Sparsity Removal Approach and Fuzzy C-Means Clustering Technique on a Movie Recommendation System

Collaborative Filtering (CF) approach has been used in many recommendation systems. Despite its popularity, CF faces several challenges such as data sparsity and scalability. In this paper, we propose a novel clustered item-based CF to solve both problems. To overcome the sparsity issue of rating da...

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Bibliographic Details
Published in2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM) pp. 329 - 334
Main Authors Ifada, Noor, Prasetyo, Eko Hadi, Mula'ab
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2018
Subjects
Clustering algorithms
Collaborative Filtering
Correlation
Filtering
Fuzzy C-Means Clustering
Genre similarity
Mathematical model
Motion pictures
Rating similarity
Scalability
Sparse matrices
Sparsity
Online AccessGet full text
DOI10.1109/CENIM.2018.8711270

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Summary:Collaborative Filtering (CF) approach has been used in many recommendation systems. Despite its popularity, CF faces several challenges such as data sparsity and scalability. In this paper, we propose a novel clustered item-based CF to solve both problems. To overcome the sparsity issue of rating data, we propose a novel sparsity removal approach that employs the combination of rating and movie genre similarities. To overcome the scalability issue, we apply the use the Fuzzy C-Means clustering technique to create groups of movies. Evaluating the proposed method on a real-world movie dataset, we show that our proposed method produces a dense rating data, is scalable for high dimensional data, and improves the recommendation quality of the traditional item-based CF method.
DOI:10.1109/CENIM.2018.8711270