Paper Recommendation Method based on Attention Mechanism and Graph Neural Network

Main Article Content

Ailin Li, Rong Jing, Qi Guo, Bin Wei


At present, most recommendation technologies only consider text or citation information, which suffers from data sparseness and cold start problems. Therefore, an academic paper recommendation method based on attention mechanism and heterogeneous graph CAH is proposed. This method considers textual information and heterogeneous graph structure information to obtain a richer and more complete feature representation. Finally, cosine similarity is calculated to generate recommendations. The results show that compared with the content-based recommendation method, the accuracy rate, recall rate and f value of CAH method are increased by nearly 5.6%, 5.8% and 8.7%, respectively, which are significantly improved compared with the basic method. This method is expected to promote the in-depth application of recommendation systems in the field of artificial intelligence.

Article Details

Author Biography

Ailin Li, Rong Jing, Qi Guo, Bin Wei

[1]Ailin Li

2Rong Jing

3Qi Guo

4,*Bin Wei


[1] School of Artificial Intelligence, Gansu University of Political Science and Law, Lanzhou City, China

2 Key Laboratory of Linguistic and Cultural Computing Ministry of Education, Northwest Minzu University, Lanzhou City, China

3 Dalian Meteorological Bureau, Dalian Meteorological Information Center, Dalian, China

4 Key Laboratory of Linguistic and Cultural Computing Ministry of Education, Northwest Minzu University, Lanzhou City, China

*Corresponding author: Bin Wei

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