Personalized movie rating prediction based on the data of short video users and videos information

Abstract

This study presents a sophisticated personalized movie rating prediction model that incorporates user-generated content from Douyin, a prominent video-based social media platform. The proposed model effectively leverages user demographic data, movie metadata, historical rating records, and sentiment analysis of user comments to precisely predict a user's rating score for films. This study employs lexicon-integrated two-channel CNN-LSTM family models for conducting sentiment analysis. Furthermore, the study explores a variety of regression models, such as linear regression, ridge regression, LASSO regression, and Elastic Net regression, to determine the most suitable model for the dataset being analyzed. This analysis aims to enhance the accuracy and effectiveness of the sentiment analysis performed on the given dataset. Furthermore, we contemplate the application of collaborative filtering algorithms, such as Alternating Least Squares (ALS), in developing our personalized movie rating prediction model. By incorporating user-generated content from Douyin, our methodology enhances the accuracy of movie rating predictions and underscores the significance of personalized recommendations in the era of social media.