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To solve the problem of lacking user ratings and inaccuracy of user topics model for danmaku video website, this paper proposed a danmaku video recommendation algorithm combing topic model and collaborative filtering ( DRCIT). Firstly, it constructed an implicit rating matrix with user behavior, and obtained the similarity of project rating. Secondly, it established an LDA topic model to generate project topics and calculate the similarity of project topics. Thirdly, it generated a comprehensive project similarity, and calculated the user-project prediction score. Then, through sigmoid penalty, it established the new similarity matrix of project topics and got user-project topic similarity combined with user history behavior. Finally, it took user-project topic similarity as weight to multiply with prediction score to get the final prediction score. The comparison experiments show that the algorithm can get reasonable recommendation results and improve the accuracy of recommendation. [ABSTRACT FROM AUTHOR]

This paper proposed a linear regression algorithm to integrate collaborative filtering based on the data sparse influence of the traditional collaborative filtering algorithm. Firstly, it built a similarity matrix between the user and the project based on the user’s rating of the project, as well as the user and the project’s own characteristics. Secondly, based on the similarity matrix, it selected the user and project nearest neighbor set. It predicted the score that the users had graded respectively by the way of collaborative filtering algorithms based on the user and the project. And it would take the difference between predicted scores and the real scores as features to generate new training data, and regarded the new training data as the input of the linear regression model. Finally, according to the training model, it could predict the unknown score, and used the top-N algorithm to generate the recommended list. It conducted the experiment on the MovieLens data set. The experimental result shows that the proposed accuracy of the new algorithm improves compared with the traditional collaborative filtering algorithm. [ABSTRACT FROM AUTHOR]

The efficiency of traditional user-based collaborative filtering (user-based CF) recommendation algorithm is reduced with data increasing. This paper proposes a recommendation algorithm based on community detection (RACD) in bipartite networks by introducing bipartite network community detection theory into user-based CF recommendation algorithm. Firstly， the user-item rating matrix is mapped into user-item bipartite network. Then， the community information of each user is obtained by using RACD to divide the user-item network. Finally， the items are recommended to the target user according to other users in the same community. Experiments on real-world classic network datasets show that the RACD can effectively improve real-time recommendation efficiency of the recommendation system. [ABSTRACT FROM AUTHOR]