基于LDA主题扩展个性化电影推荐系统.pdf

Computer Science and Application 计算机科学与应用, 2018, 8(6), 860-866 Published Online June 2018 in Hans. /journal/csa /10.12677/csa.2018.86095 Personalized Movie Recommendation System Based on LDA Theme Extension Ping Cui, Li Song, Xinkai Yang College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai th th th Received: May 24 , 2018; accepted: Jun. 8 , 2018; published: Jun. 15 , 2018 Abstract Traditional movie recommendation algorithms based on user score data have some problems, such as sparse data and false score information, which cannot really and effectively express user interest. User comments, as an effective carrier of information from users’ interests and opinions, can quantify the product features through mining, analyzing and commenting information, and achieve personalized recommendation effect. Site review analysis of text information based on feature film recommended to the users in time according to the user history information analysis. User interest recommendation algorithm is proposed for expanding fusion sentiment analysis and LDA topic model features personalized selection keywords combined with TF-IDF weight item keywords and the characteristics development; the positive rate of sentiment analysis combined with topic comment expands the feature vector for item similarity calculation; the user is inter- ested in the high similarity of the product as the recommended items list recommended. Experi- ments show that the proposed method improves the accuracy of recommendation. Keywords Recommendation System, Topic Model, Sentiment Analysis, Text Mining 基于LDA主题扩展的个性化电影推荐系统 崔 苹，宋 丽，杨新凯