Open Access

ARTICLE

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

by G. Parthasarathy^1,*, S. Sathiya Devi²

1 Anna University, Chennai, 600025, India
2 University College of Engineering, BIT Campus, Anna University, Tiruchirappalli, 620024, India

* Corresponding Author: G. Parthasarathy. Email:

Computers, Materials & Continua 2022, 71(2), 2419-2434. https://doi.org/10.32604/cmc.2022.019805

Received 26 April 2021; Accepted 24 June 2021; Issue published 07 December 2021

Abstract

Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.

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Keywords

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