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Meta-Graph Based Attention-Aware Recommendation over Heterogeneous Information Networks

  • Feifei Dai
  • Xiaoyan GuEmail author
  • Bo Li
  • Jinchao Zhang
  • Mingda Qian
  • Weiping Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11537)

Abstract

Heterogeneous information network (HIN), which involves diverse types of data, has been widely used in recommender systems. However, most existing HINs based recommendation methods equally treat different latent features and simply model various feature interactions in the same way so that the rich semantic information cannot be fully utilized. To comprehensively exploit the heterogeneous information for recommendation, in this paper, we propose a Meta-Graph based Attention-aware Recommendation (MGAR) over HINs. First of all, MGAR utilizes rich meta-graph based latent features to guide the heterogeneous information fusion recommendation. Specifically, in order to discriminate the importance of latent features generated by different meta-graphs, we propose an attention-based feature enhancement model. The model enables useful features and useless features contribute differently to the prediction, thus improves the performance of the recommendation. Furthermore, to holistically exploit the different interrelation of features, we propose a hierarchical feature interaction method which consists three layers of second-order interaction to mine the underlying correlations between users and items. Extensive experiments show that MGAR outperforms the state-of-the-art recommendation methods in terms of RMSE on Yelp and Amazon Electronics.

Keywords

Recommendation Meta-graph Heterogeneous information networks Attention model Feature interaction 

Notes

Acknowledgement

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDC02050200) and Beijing Municipal Science and Technology Project (Z181100002718004).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Feifei Dai
    • 1
    • 2
  • Xiaoyan Gu
    • 1
    Email author
  • Bo Li
    • 1
  • Jinchao Zhang
    • 1
  • Mingda Qian
    • 1
  • Weiping Wang
    • 1
  1. 1.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  2. 2.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina

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