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2018 | OriginalPaper | Buchkapitel

An Improved Collaborative Filtering Recommendation Algorithm for Big Data

verfasst von : Hafed Zarzour, Faiz Maazouzi, Mohamed Soltani, Chaouki Chemam

Erschienen in: Computational Intelligence and Its Applications

Verlag: Springer International Publishing

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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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Titel: An Improved Collaborative Filtering Recommendation Algorithm for Big Data
verfasst von: Hafed Zarzour
Faiz Maazouzi
Mohamed Soltani
Chaouki Chemam
Verlag: Springer International Publishing
Buch: Computational Intelligence and Its Applications
Print ISBN: 978-3-319-89742-4

Electronic ISBN: 978-3-319-89743-1

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-89743-1_56

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