Open Access

ARTICLE

Movie Recommendation Algorithm Based on Ensemble Learning

by Wei Fang^1,2,*, Yu Sha¹, Meihan Qi¹, Victor S. Sheng³

1 School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
3 Department of Computer, Texas Tech University, Lubbock, TX 79409, USA

* Corresponding Author: Wei Fang. Email:

Intelligent Automation & Soft Computing 2022, 34(1), 609-622. https://doi.org/10.32604/iasc.2022.027067

Received 10 January 2022; Accepted 15 March 2022; Issue published 15 April 2022

Abstract

With the rapid development of personalized services, major websites have launched a recommendation module in recent years. This module will recommend information you are interested in based on your viewing history and other information, thereby improving the economic benefits of the website and increasing the number of users. This paper has introduced content-based recommendation algorithm, K-Nearest Neighbor (KNN)-based collaborative filtering (CF) algorithm and singular value decomposition-based (SVD) collaborative filtering algorithm. However, the mentioned recommendation algorithms all recommend for a certain aspect, and do not realize the recommendation of specific movies input by specific users which will cause the recommended content of the website to deviate from the need of users, and affect the experience of using. Aiming at this problem, this paper combines the above algorithms and proposes three ensemble recommendation algorithms, which are the ensemble recommendation of KNN + text, the recommendation of user KNN + movie KNN, and the recommendation of user KNN + singular value decomposition. Compared with the traditional collaborative filtering algorithm based on matrix factorization, the method we proposed can realize the recommendation of specific movies input by specific users and make more personalized recommendations and can deal with the problem of cold start and sparse matrix processing issues to a certain extent.

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Keywords

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