An Improved Collaborative Filtering Recommendation Algorithm for Big Data

Hafed Zarzour,Mohamed Soltani,Chaouki Chemam,Faiz Maazouzi

doi:10.1007/978-3-319-89743-1_56

Hafed Zarzour, Mohamed Soltani + Show 2 more

Open Access

https://doi.org/10.1007/978-3-319-89743-1_56

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Publication Date: Jan 1, 2018
Citations: 22	License type: cc-by

Affiliation: Mohamed-Cherif Messaadia University

Abstract

With the increase of volume, velocity, and variety of big data, the traditional collaborative filtering recommendation algorithm, which recommends the items based on the ratings from those like-minded users, becomes more and more inefficient. In this paper, two varieties of algorithms for collaborative filtering recommendation system are proposed. The first one uses the improved k-means clustering technique while the second one uses the improved k-means clustering technique coupled with Principal Component Analysis as a dimensionality reduction method to enhance the recommendation accuracy for big data. The experimental results show that the proposed algorithms have better recommendation performance than the traditional collaborative filtering recommendation algorithm.

Highlights

With the explosive increase in available data on the web and the rapid advances of information technology, big data has become a hot research topic in the field of data mining
To evaluate the performance of the k-means clustering-based collaborative filtering recommendation algorithm with and without using Principal Component Analysis (PCA) compared to traditional collaborative filtering recommendation algorithm, experimentations were conducted on real big data
We have presented two kinds of improved collaborative filtering algorithms intended to enhance the prediction accuracy in the big data context

Summary

Introduction

With the explosive increase in available data on the web and the rapid advances of information technology, big data has become a hot research topic in the field of data mining. Many governmental and industrial communities become interested in the high potential of this innovative technology It is very difficult for such communities to find relevant contents, recommender systems appear to solve present problems. Many researches have reported that applying k-means as clustering technique in collaborative recommender systems can significantly enhance the performance of traditional CFRA[6].it has been proved that using Principal Component Analysis (PCA) as a dimensionality reduction method can significantly improve the clustering techniques[7], it is necessary to conduct dimensions reeducation before formally conducting clustering tasks. In this paper, we propose two varieties of algorithms for an effective collaborative filtering recommendation system. The experimental results show that the proposed algorithms have better recommendation performance than the traditional collaborative filtering recommendation algorithm.

Related Work

Collaborative filtering recommendation algorithm

K-means based-collaborative filtering algorithm

K-means algorithm

CFRA-Km: a collaborative filtering recommendation algorithm based on K-means clustering

Reducing the dimension by PCA

CFRA-Km-PCA: a collaborative filtering recommendation algorithm based on K-means clustering and PCA

Experimentation results and evaluation

Findings

Conclusion and future work