JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (11): 35-45.doi: 10.6040/j.issn.1671-9352.1.2019.017

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Academic paper recommendation based on heterogeneous network embedding

Kan XU(),Rui-xin LIU,Hong-fei LIN,Hai-feng LIU,Jiao-jiao FENG,Jia-ping LI,Yuan LIN*(),Bo XU   

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
  • Received:2019-10-14 Online:2020-11-20 Published:2020-11-17
  • Contact: Yuan LIN E-mail:xukan@dlut.edu.cn;zhlin@dlut.edu.cn

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Abstract:

This paper focuses on the extraction and utilization of information from heterogeneous information network recommendation, and proposes a heterogeneous network embedding-based academic paper recommendation method. The proposed method uses the random walk strategy guided by the meta-path to generate the sequence of nodes. Then, for each meta-path, the unique embedding representation of nodes is learned by maximizing its co-occurrence probability with the adjacent nodes in the sequence obtained from the given meta-path. In addition, in order to fuse the low-dimensional representation of nodes in differentmetapaths into the final output of heterogeneous information network embedding, the different fusion functions are designed to achieve this goal, and attention mechanism is introduced to recommendation system. This paper applies the heterogeneous information network based on meta paths to solve the problem that most recommendation methods based on heterogeneous information networks rely on path similarity, which can not fully mine the potential structural characteristics of users and projects. We use DBLP data set to validate the effectiveness of the proposed model, which achieved better performance compared with the traditional model on RMSE.

Key words: heterogeneous information network, meta path, network embedding, paper recommend

CLC Number: 

  • TP391.1

Fig.1

Model fundamental"

Fig.2

Attention model fundamental"

Fig.3

Meta-path based random walk fundamentalfor academic paper information network"

Table 1

DBLP data set"

实体或关系名称 数量
作者 14 475
论文 14 376
作者标签 4
会议 20
论文类型 8 920
作者-标签 4 057
论文-作者 41 794
论文-会议 14 376
论文-类型 114 624

Fig.4

Effectiveness of iteration step size to convergence"

Fig.5

Effectiveness of α、β to the experimental results"

Fig.6

Effect of dimension d of latent factor on the experimental results"

Fig.7

Effect of learning rate and fusion function to the experimental results"

Table 2

Comparison of different models"

学习率/% PMF libFM AttHercs AttHercal AttHercasl
80 0.502 8 0.421 9 0.560 5 0.446 4 0.420 6
60 0.662 6 0.472 6 0.631 0 0.509 6 0.462 4
40 0.800 2 0.556 1 0.704 0 0.554 8 0.566 0
20 0.988 6 0.673 2 0.825 0 0.712 0 0.698 8
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