A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation syst...

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Bibliographic Details
Published in:Journal of electrical and computer engineering 2019-01, Vol.2019 (2019), p.1-12
Main Authors: Liu, Yanbing, Ma, Chuang, Tang, Zhijing, Xu, Guangxia, Daneshmand, Mahmoud
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Behavior
Clustering
Collaboration
Filtration
Flooding
Human behavior
Information retrieval
Information science
International conferences
Knowledge management
Methods
Neighborhoods
Preferences
Recommender systems
Search engines
Similarity
Similarity measures
Sparsity
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Summary:Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.
ISSN:2090-0147
2090-0155
DOI:10.1155/2019/7070487