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Xiaole Wang, Jiwei Qin, Shangju Deng and Wei Zeng
In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user a...
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Yi Liu, Chengyu Yin, Jingwei Li, Fang Wang and Senzhang Wang
Accurately predicting user?item interactions is critically important in many real applications, including recommender systems and user behavior analysis in social networks. One major drawback of existing studies is that they generally directly analyze th...
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Jianfei Li, Yongbin Wang and Zhulin Tao
In recent years, graph neural networks (GNNS) have been demonstrated to be a powerful way to learn graph data. The existing recommender systems based on the implicit factor models mainly use the interactive information between users and items for trainin...
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Ninghua Sun, Tao Chen, Wenshan Guo and Longya Ran
The problems with the information overload of e-government websites have been a big obstacle for users to make decisions. One promising approach to solve this problem is to deploy an intelligent recommendation system on e-government platforms. Collaborat...
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Bo Wang, Feiyue Ye and Jialu Xu
A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user ra...
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