TY - JOUR
T1 - A collaborative filtering recommendation algorithm based on user confidence and time context
AU - Xu, Guangxia
AU - Tang, Zhijing
AU - Ma, Chuang
AU - Liu, Yanbing
AU - Daneshmand, Mahmoud
N1 - Publisher Copyright:
© 2019 Guangxia Xu et al.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
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U2 - 10.1155/2019/7070487
DO - 10.1155/2019/7070487
M3 - Article
AN - SCOPUS:85068663829
SN - 2090-0147
VL - 2019
JO - Journal of Electrical and Computer Engineering
JF - Journal of Electrical and Computer Engineering
M1 - 7070487
ER -