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Personalized Recommendation Mechanism Based on Collaborative Filtering in Cloud Computing Environment
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Author(s): Xinling Tang (School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou, China), Hongyan Xu (Department of Public Science Education Committee, Zhengzhou Shuqing Medical College, Henan Zhengzhou, China), Yonghong Tan (School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou, China)and Yanjun Gong (School of Electronics and Information Engineering, Hunan University of Science and Engineering, Yongzhou, China)
Copyright: 2021
Pages: 19
Source title:
Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-5339-8.ch035
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Abstract
With the advent of cloud computing era and the dramatic increase in the amount of data applications, personalized recommendation technology is increasingly important. However, due to large scale and distributed processing architecture and other characteristics of cloud computing, the traditional recommendation techniques which are applied directly to the cloud computing environment will be faced with low recommendation precision, recommended delay, network overhead and other issues, leading to a sharp decline in performance recommendation. To solve these problems, the authors propose a personalized recommendation collaborative filtering mechanism RAC in the cloud computing environment. The first mechanism is to develop distributed score management strategy, by defining the candidate neighbors (CN) concept screening recommended greater impact on the results of the project set. And the authors build two stage index score based on distributed storage system, in order to ensure the recommended mechanism to locate the candidate neighbor. They propose collaborative filtering recommendation algorithm based on the candidate neighbor on this basis (CN-DCF). The target users are searched in candidate neighbors by the nearest neighbor k project score. And the target user's top-N recommendation sets are predicted. The results show that in the cloud computing environment RAC has a good recommendation accuracy and efficiency recommended.
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