A Recommended Method Based on the Weighted RippleNet Network Mode
Yutai Luo, Baocheng Sha, Tao Xu · Journal of Physics Conference Series · 2021
Abstract 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.