Open AccessA Recommended Method Based on the Weighted RippleNet Network Mode
Author(s) -
Yutai Luo,
Baocheng Sha,
Tao Xu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2025/1/012011
Subject(s) - subnet , computer science , recommender system , graph , complex network , data mining , theoretical computer science , artificial intelligence , machine learning , computer network , world wide web
User preferences were modeled by the RippleNet network and successfully applied in the recommender systems, but the weight of the entity was not considered. This paper proposes a RippleNet model incorporating the influence of complex network nodes. After the construction of complex networks based on knowledge Graphs, we build the maximum subnet model and calculate the influence of nodes in the graph network. We added it to the RippleNet as the weight of entities. The experimental results showed that new method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, solve the problem that entity influence was not considered in the RippleNet network, and made the recommended results more in line with users’ expectations.