A Movie Recommender System Based on Topic Modeling using Machine Learning Methods
Publish place: International Journal of Web Research، Vol: 5، Issue: 2
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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JR_IJWR-5-2_003
تاریخ نمایه سازی: 20 دی 1401
Abstract:
In recent years, we have seen an increase in the production of films in a variety of categories and genres. Many of these products contain concepts that are inappropriate for children and adolescents. Hence, parents are concerned that their children may be exposed to these products. As a result, a smart recommendation system that provides appropriate movies based on the user's age range could be a useful tool for parents. Existing movie recommender systems use quantitative factors and metadata that lead to less attention being paid to the content of the movies. This research is motivated by the need to extract movie features using information retrieval methods in order to provide effective suggestions. The goal of this study is to propose a movie recommender system based on topic modeling and text-based age ratings. The proposed method uses latent Dirichlet allocation (LDA) modelling to identify hidden associations between words, document topics, and the levels of expression of each topic in each document. Machine learning models are then used to recommend age-appropriate movies. It has been demonstrated that the proposed method can determine the user's age and recommend movies based on the user's age with ۹۳% accuracy, which is highly satisfactory.
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Authors
Mojtaba Kordabadi
MSc, Computer Engineering Department, Bu-Ali Sina University, Hamedan, Iran
Amin Nazari
Ph.D. Candidate, Computer Engineering Department, Bu-Ali Sina University, Hamedan, Iran
Muharram Mansoorizadeh
Associate Professor, Computer Engineering Department, Bu-Ali Sina University