Movie Recommendation System based on Latent Factor Model
- Resource Type
- Article
- Authors
- Chen Ma; 김강철
- Source
- 한국전자통신학회 논문지, 16(1), pp.125-134 Feb, 2021
- Subject
- 전자/정보통신공학
- Language
- English
- ISSN
- 1975-8170
With the rapid development of the film industry, the number of films is significantly increasing and users spend long time reading movie reviews to find their favorite ones. Movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.