A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm

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...

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Bibliographic Details
Published in:Complex & intelligent systems 2021-02, Vol.7 (1), p.297-309
Main Authors: Tripathi, Ashish Kumar, Mittal, Himanshu, Saxena, Pranav, Gupta, Siddharth
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Collaboration
Complexity
Computational Intelligence
Data Structures and Information Theory
Datasets
Decision making
Engineering
Optimization algorithms
Original Article
Particle swarm optimization
Recommender systems
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Summary: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.
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-020-00200-0