Affiliations: [a] School of Software, Shandong University, Jinan, Shandong, China | [b] Dareway Software Co., Ltd, Jinan, Shandong, China
Correspondence:
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Corresponding author: Yuliang Shi, School of Software, Shandong University, Jinan, Shandong 250000, China. E-mail: shiyuliang@sdu.edu.cn.
Abstract: Knowledge graph (KG) has been widely used in the field of recommender systems. There are some nodes in KG that guide the occurrence of interaction behaviors. We call them guided nodes. However, the current application doesn’t take into account the guided nodes in KG. We explore the utility of guided nodes in KG. It is applied in repository recommendations. In this paper, we propose an end-to-end framework, namely Guided Node Graph Convolutional Network (GNGCN), which effectively captures the connections between entities by mining the influence of related nodes. We extract samples of each entity in KG as their guided nodes and then combine the information and bias of the guided nodes when computing the representation of a given entity. The guided nodes can be extended to multiple hops. We evaluate our model on a real-world Github dataset named Github-SKG and music recommendation dataset, and the experimental results show that the method outperforms the recommendation baselines and our model is much lighter than others.
