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Traditional content-based recommendation algorithm has lower accuracy, while data sparseness and cold start problems are common in collaborative filtering recommendation algorithms. To solve this problem, this paper proposed a hybrid recommendation algorithm based on content and collaborative matrix factorization technique. The algorithm realized the decomp osition of content and collaborative matrix in a common low-dimensional space while preserving the local data structure. This paper used an iterative method based on multiplication update rules in parameter optimization, improved learning ability. The experimental results show that the proposed algorithm is superior to other representative projects cold start recommendation algorithm, which effectively alleviates the data sparseness and improves the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

In order to solve the problems such as sparse data, cold start and lack of diversity of recommendation results in traditional collaborative filtering recommendation algorithms, this paper proposed a collaborative filtering recommendation algorithm that integrated social networking with key users . Based on the score matrix of user projects, the proposed algorithm integrated user social networks to de rive social trust matrix, integrated key user information to obtain key user scoring matrix. And then it used these three matrix data distributions to predict user's target project with different weights score. At the same time, aiming at the massive data problem, this paper used the Spark distributed cluster to realize the parallelization of the algorithm. The experimental results show that the proposed algorithm can effectively alleviate the data sparse problem and improve the data processing speed and recommendation accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Collaborative filtering has a sparse problem in data processing, which affects the accuracy of the recommendation algorithm. This paper proposed a recommendation algorithm combining collaborative filtering and XGBoost to explore the potential relationship between the project and the user based on the user's evaluation of the project and its own characteristics. It improved the recommendation accuracy of the algorithm. The results of experiments on the book-crossings data set using the Baidu deep learning framework PaddlePaddle show that, compared with the two algorithms in the literatures, the accuracy of the proposed algorithm is significantly improved. [ABSTRACT FROM AUTHOR]

Aiming at optimizing and improving a hybrid collaborative filtering based on Spark platform for its sparsity, scalability and personalized recommendation by using the method of algorithm integration, this paper took the model of Stacking integration to integrate multiple weak recommender units in a linearly weighted into a comprehensive recommender. Firstly, this algorithm optimized the collaborative filtering based on the nearest neighbor by presorting and adjusting the similarity calculation strategy with popularity and praise degree, and improved the rationality and complexity of similarity calculation. It solved the problem of score sparsity to some extent. At the same time, this algorithm integrated closely distributed computing platform, which could make full use of the advantages of distributed platform to design and implement an increment iterative model of recommendation algorithm by using the Spark streaming and distributed storage structure. It solved the problem that collaborative filtering algorithm was hard to expand and made poor real-time performance. The experimental data used UCI public data set named MovieLens and NetFlix films’ score. The experimental results show that the improved algorithm has a good performance and makes great progress in personalized recommendation, accuracy and scalability compared with the previous algorithms. It provides a feasible algorithm integration scheme for the application of the recommended system. [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]

According to the sparse characteristics of big data, singular value decomposition method was introduced into collaborative filtering algorithm to calculate and verify the page interest degree of internal site users, this paper proposed a user page interest prediction algorithm based on improved collaborative filtering algorithm. The algorithm could extract the false score in the dominant user score data in the network log file, and found user page interest and influence factors. The results of MATLAB simulation show that the collaborative filtering algorithm based on singular value decomposition is accurate and efficient in predicting the interest degree of user pages under the unavoidable situation of massive data sparsity. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most algorithms for removing impulse noise adopt the predefined global threshold to restore the corrupted images,therefore,they are not good at the recovery of local edges and details in images. Thus,this paper provided a regularization filtering algorithm based on edges’ characteristics. The algorithm built an adaptive threshold by the local statistics which could reflect the edges and details in images. It adopted these thresholds to detect noisy pixels and got a candidate set filled of noise.Then it used the candidate set to optimize the edge-preserving regularization method for its performance. Simulation results show that proposed algorithm still can provide better performance of restoration by quantitative comparison and subjective visual comparison when noise level is high,thereby the algorithm can provide a good base for further research. [ABSTRACT FROM AUTHOR]