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There are some shortcomings in user-based collaborative filtering, this paper proposed a new collaborative filtering recommendation algorithms, named user's gravitation based collaborative filtering (UGBCF) recommendation algorithm, it used a new method of similarity measure to improve the user-based collaborative recommendation algorithms. This paper thought that the social tags used by user can reflect user’s preference and how much the preference, so it used those social tags to build user's preference object model. It computed the gravitation between preference objects, viewed the gravitation as the similarity of users. According to the similarity, the neighbor user of the target user could be gotten, and the prediction score of his unselected items could be calculated by aggregated the neighbor users' score. The results of experiment show that UGBCF can provide better recommendation quality than other collaborative filtering recommendation. [ABSTRACT FROM AUTHOR]

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]

Recommender systems can effectively reduce the information overload by recommending items that users may be interested in. Euclidean-embedding-based collaborative filtering methods map users and items to a unified latent space, which is one of the most important methods to build a recommender system. However, traditional Euclidean-embedding-based collaborative filtering methods only consider the low-order interaction between user latent feature vectors and item latent feature vectors, and cannot efficiently model the complex interaction behavior between users and items in the real world. In this paper, we propose a deep-Euclidean-embedding-based collaborative filtering algorithm, which utilizes deep learning technology to learn the high-order and nonlinear interaction function between user latent feature vectors and item latent feature vectors. This can model the complex interaction behavior between users and items. Experimental results on real-world datasets show that our proposed algorithm outperforms traditional collaborative filtering algorithms. [ABSTRACT FROM AUTHOR]

This paper proposed an approach called RQE-EDKL (recommendation quality evaluation based on empirical distribution and KL divergence) to evaluate the recommendation quality based on empirical distribution and KL divergence. QE-EDKL firstly made use of historical user-item data to produce the historical usage probability distribution of items at different quantities. Secondly, it calculated the KL divergence based on the distributions of the historical usage probability and the usage probability of different recommendations. Thirdly, it regarded the recommendation with the minimum KL divergence as with the best quality and is recommended to the user. Experiments on TalkingData App data sets demonstrate that RQE-EDKL can effectively improve the quality of recommended results of collaborative filtering significantly on both accuracy and diversity. [ABSTRACT FROM AUTHOR]

Aiming at the problem of ignores the users interest in the key words and the data sparseness in traditional collaborative filtering algorithm. This paper proposed a collaborative filtering recommendation algorithm combined with the user interest degree clustering, and used user ratings for projects and extracting keywords from item attributes. It proposed a new rating frequency-inverse item frequency algorithm. According to the target users' scoring frequency for a key word and the frequency of the keyword being evaluated by all users, it got users' preferences for keywords, formed user preference matrix, and clustered on the basis of this matrix. Then it used logistic function to get users' interest in projects, cleared user preferences and found similar users of target users in the clusters. Then extracted N items from neighbors' preferences, and recommended users. Experimental results show that the algorithm accuracy rate is always better than the traditional algorithm. It's more accurate to judge the user interest, alleviating the problem of data sparseness, and effectively improves the accuracy and efficiency 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]

In modern information society, recommendation systems had already been widely used. Traditional recommendation algorithm didn't take users' emotion tendencies into consideration. To aim at the problem of improving the performance of the recommendation system, this paper proposed an improved recommendation algorithm on the basis of traditional collaborative filtering algorithm. This algorithm combined with emotion analysis. As the user's reviews more directly reflected their real fee-lings, it was feasible to generate the corresponding scores by analyzing the emotional tendencies of user reviews to replace the scores which was given by users. The improved algorithm has certain originality. The experiment shows that this recommendation algorithm improves the performance of the recommendation system, with positive significance for the development of the recommendation system. [ABSTRACT FROM AUTHOR]