Volume 39, Number 5, October 2021
|Page(s)||1070 - 1076|
|Published online||14 December 2021|
Recommended method study based on incorporating complex network ripple net
Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou 730030, China
The RippleNet network models user preferences and is well applied in the recommended system. But Ripplenet didn't take into account the weight of entities in the knowledge graph, resulting in the inaccurate recommendation results. A RippleNet model incorporating the influence of the complex network nodes is proposed. After constructing the complex networks based on the knowledge maps, the maximum subnet model is extracted, the influence of the nodes in the map network is calculated, and the weight of the nodes is added to the RippleNet model as an entity. The experimental results showed that the present method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, made up for the problem that no entity influence was considered in the RippleNet network, and made the recommended results more in line with users' expectations.
Key words: knowledge graph / recommended system / complex network / node influence / rippleNet
关键字 : 知识图谱 / 推荐系统 / 复杂网络 / 节点影响力 / RippleNet
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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