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With the advent and explosive growth of the Web over the past decade, recommender systems have become at the heart of the business strategies of e-commerce and Internet-based companies such as Google, YouTube, Facebook, Netflix, LinkedIn, Amazon, etc. Hence, the collaborative filtering recommendation algorithms are highly valuable and play a vital role at the success of such businesses in reaching out to new users and promoting their services and products. With the aim of improving the recommendation performance of such an algorithm, this paper proposes a new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. The ¿-means algorithm and Singular Value Decomposition (SVD) are both used to cluster similar users and reduce the dimensionality. It proposes and evaluates an effective two stage recommender system that can generate accurate and highly efficient recommendations. The experimental results show that this new method significantly improves the performance of the recommendation systems.

Item Type	Conference
Divisions	» Laboratory of Computer Science and Mathematics » Faculty of Science and Technology
ePrint ID	1248
Date Deposited	2018-05-12
Further Information	Google Scholar
URI	https://univ-soukahras.dz/en/publication/article/1248

@inproceedings{uniusa1248,
    title={A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques},
    author={Hafed ZARZOUR, Ziad Al-Sharif, Mahmoud Al-Ayyoub and Yaser Jararweh},
    year={2018},
    booktitle={IEEE 9th International Conference on Information and Communication Systems (ICICS)}
}