Scientific Publications

Download 

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.

Item Type	Conference
Divisions	» Faculty of Science and Technology
ePrint ID	1249
Date Deposited	2018-05-12
Further Information	Google Scholar
URI	https://univ-soukahras.dz/en/publication/article/1249

@inproceedings{uniusa1249,
    title={An Improved Collaborative Filtering Recommendation Algorithm for Big Data},
    author={Hafed ZARZOUR, Faiz Maazouzi, Mohamed SOLTANI and Chaouki Chemam},
    year={2018},
    booktitle={In: Amine A., Mouhoub M., Ait Mohamed O., Djebbar B. (eds) Computational Intelligence and Its Applications. CIIA 2018. IFIP Advances in Information and Communication Technology, vol 522. Springer, Cham}
}