期刊
COMPLEX & INTELLIGENT SYSTEMS
卷 7, 期 1, 页码 297-309出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s40747-020-00200-0
关键词
Recommendation system; Big data; Map-reduce; Clustering; Whale optimization algorithm
In the era of Web 2.0, the growing data volume aids E-commerce websites in making better decisions. A map-reduce-based clustering recommendation system is proposed, utilizing a novel variant of the whale optimization algorithm to improve clustering efficiency.
In the era of Web 2.0, the data are growing immensely and is assisting E-commerce websites for better decision-making. Collaborative filtering, one of the prominent recommendation approaches, performs recommendation by finding similarity. However, this approach fails in managing large-scale datasets. To mitigate the same, an efficient map-reduce-based clustering recommendation system is presented. The proposed method uses a novel variant of the whale optimization algorithm, tournament selection empowered whale optimization algorithm, to attain the optimal clusters. The clustering efficiency of the proposed method is measured on four large-scale datasets in terms of F-measure and computation time. The experimental results are compared with state-of-the-art map-reduce-based clustering methods, namely map-reduce-based K-means, map-reduce-based bat algorithm, map-reduce-based Kmeans particle swarm optimization, map-reduce-based artificial bee colony, and map-reduce-based whale optimization algorithm. Furthermore, the proposed method is tested as a recommendation system on the publicly available movie-lens dataset. The performance validation is measured in terms of mean absolute error, precision and recall, over a different number of clusters. The experimental results assert that the proposed method is a permissive approach for the recommendation over large-scale datasets.
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