An Improved Collaborative Filtering Recommendation Algorithm for Big Data

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.
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Abdelmalek Amine; Malek Mouhoub; Otmane Ait Mohamed; Bachir Djebbar. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-522, pp.660-668, 2018, Computational Intelligence and Its Applications. 〈10.1007/978-3-319-89743-1_56〉
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Hafed Zarzour, Faiz Maazouzi, Mohamed Soltani, Chaouki Chemam. An Improved Collaborative Filtering Recommendation Algorithm for Big Data. Abdelmalek Amine; Malek Mouhoub; Otmane Ait Mohamed; Bachir Djebbar. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-522, pp.660-668, 2018, Computational Intelligence and Its Applications. 〈10.1007/978-3-319-89743-1_56〉. 〈hal-01913907〉

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