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Aggregating knowledge-aware graph neural network and adaptive relational attention for recommendation

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Abstract

Currently, recommender systems based on knowledge graph (KG) consider various aspects of the item to provide accurate recommendations. Many studies have shown that exploiting the rich semantics of KG is effective to improve recommendation performance, and can solve data sparsity and provide interpretable recommendation. However, most existing KG-based recommender systems ignore the fact that users attach different degrees of importance to various relationships of items. To tackle this problem, we propose a knowledge graph recommender model based on adaptive relational attention (KGARA), which can capture the attention of various users to different relationships of items. Specifically, we introduce the relation embedding to model the semantic information of KG, and capture the user’s attention on each relation of the targeted item with the attention mechanism. In addition, we introduce receptive fields to select neighbor nodes of the target node in the KG, which greatly alleviate computational burden. Extensive experiments on three real-world datasets demonstrate that the proposed algorithm has significant improvements over other state-of-the-art algorithms.

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Notes

  1. https://grouplens.org/datasets/movielens/1m/

  2. https://grouplens.org/datasets/hetrec-2011/

  3. http://www.informatik.uni-freiburg.de/~cziegler/BX/

  4. https://searchengineland.com/library/bing

  5. https://github.com//haomiaocqut/ReSys_KGARA

References

  1. Gao M, Zhang J, Yu J et al (2021) Recommender systems based on generative adversarial networks: A problem-driven perspective[J]. Information Sciences, (546):1166–1185

  2. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems[J]. Computer 42(8):30–37

    Article  Google Scholar 

  3. Cheng Z, Ding Y, He X et al (2018) A3NCF: An adaptive aspect attention model for rating prediction[C]. In: Proceedings of the 27th international joint conference on artificial intelligence, pp 3748–3754

  4. Wang X, He X, Feng F et al (2018) Tem: Tree-enhanced embedding model for explainable recommendation[C]. In: Proceedings of the 27th world wide web conference, pp 1543-1552

  5. Dadoun A, Troncy R, Ratier O et al (2019) Location Embeddings for Next Trip Recommendation[C]. In: Proceedings of the 28th world wide web conference, pp 896–903

  6. Ye Y, Wang X, Yao J et al (2019) Bayes EMbedding (BEM) refining representation by integrating knowledge graphs and behavior- specific networks[C]. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 679–688

  7. Cai H, Zheng VW, Chang KCCA (2018) Comprehensive survey of graph embedding: problems, techniques, and applications[J]. IEEE Trans Knowl Data Eng 30(9):1616–1637

    Article  Google Scholar 

  8. Ma W, Zhang M, Cao Y et al (2019) Jointly learning explai nable rules for recommendation with knowledge graph[C]. In: Proceedings of the 28th World Wide Web Conference, pp 1210–1221

  9. Zhao H, Yao Q, Li J et al (2017) Meta-graph based recommendation fusion over heterogeneous information networks[C]. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 635–644

  10. Zhao J, Zhou Z, Guan Z et al (2019) Intentgc: a scalable graph convolution framework fusing heterogeneous information for recommendation[C]. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 2347–2357

  11. Wang H, Zhang F, Zhang M et al (2019) Knowledge-aware graph neural networks with label smoothness regularization for recommender systems[C]. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 968–977

  12. Wang X, He X, Cao Y et al (2019) Kgat: Knowledge graph attention network for recommendation[C]. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 950–958

  13. Zhang F, Yuan NJ, Lian D et al (2016) Collaborative knowledge base embedding for recommender systems[C]. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 353–362

  14. Wang H, Zhang F, Xie X et al (2018) DKN: Deep knowledge-aware network for news recommendation[C]. In: Proceedings of the 27th World Wide Web Conference, pp 1835–1844

  15. Kim Y (2014) Convolutional neural networks for sentence classification[C]. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1746–1751

  16. Yu X, Ren X, Gu Q et al (2013) Collaborative filtering with entity similarity regularization in heterogeneous information networks[C]. In: Proceedings of the 23th international joint conference on artificial intelligence, pp 27–35

  17. Luo C, Pang W, Wang Z et al (2014) Hete-cf: Social-based collaborative filtering recommendation using heterogeneous relations[C]. In: Proceedings of the 14th IEEE international conference on data mining, pp 917–922

  18. Yu X, Ren X, Sun Y et al (2013) Recommendation in heterogeneous information networks with implicit user feedback[C]. In: Proceedings of the 7th ACM conference on Recommender systems, pp 347–350

  19. Yu X, Ren X, Sun Y et al (2014) Personalized entity recommendation: A heterogeneous information network approach[C]. In: Proceedings of the 7th ACM international conference on Web search and data mining, pp 283–292

  20. Wang H, Zhang F, Wang J et al (2018) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems[C]. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 417–426

  21. Tang X, Wang T, Yang H et al (2019) AKUPM: Attention-enhanced knowledge-aware user preference model for recommendation[C]. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1891–1899

  22. Niepert M, Ahmed M, Kutzkov K (2016) Learning convolutional neural networks for graphs[C]. In: Proceedings of the 19th international conference on machine learning, pp 2014–2023

  23. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs[C]. In: Proceedings of the 30th advances in neural information processing systems, pp 1024–1034

  24. He X, Liao L, Zhang H et al (2017) Neural collaborative filtering[C]. In: Proceedings of the 26th international conference on world wide web, pp 173–182

  25. Wang H, Zhang F, Zhao M et al (2019) Multi-task feature learning for knowledge graph enhanced recommendation[C]. In: Proceedings of the 28th world wide web conference, pp 2000–2010

  26. Wang X, He X, Wang M et al (2019) Neural graph collaborative filtering[C]. In: Proceedings of the 42nd international ACM SIGIR conference on Research and development in information retrieval, pp 165–174

  27. Chen L, Wu L, Hong R et al (2020) Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach[C]. Proceedings of the 34-th AAAI conference on artificial intelligence 34(01):27–34

    Article  Google Scholar 

  28. Schlichtkrull M, Kipf TN, Bloem P et al (2018) Modeling relational data with graph convolutional networks[C]. In: European semantic web conference. Springer, Cham, pp 593–607

  29. Bordes A, Usunier N, Garcia-Duran A et al (2013) Translating embeddings for modeling multirelational data [J]. In: Advances in neural information processing systems, pp 2787–2795

  30. Lin Y, Liu Z, Sun M et al (2015) Learning entity and relation embeddings for knowledge graph completion[C]. In: Proceddings of the 29-th AAAI conference on artificial intelligence

  31. Ji G, He S, Xu L et al (2015) Knowledge graph embedding via dynamic mapping matrix[C]. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (Volume 1: Long Papers), pp 687–696

  32. Zhu Q, Zhou X, Wu J et al (2020) A knowledge-aware attentional reasoning network for recommendation[C]. Proceedings of the AAAI Conference on Artificial Intelligence 34(04):6999–7006

    Article  Google Scholar 

  33. Guo Q, Zhuang F, Qin C et al (2020) A survey on knowledge graph-based recommender systems[J]. IEEE Transactions on Knowledge and Data Engineering

  34. Qin D, Zhou X, Chen L et al (2018) Dynamic connection-based social group recommendation[J]. IEEE Trans Knowl Data Eng 32(3):453–467

    Article  Google Scholar 

  35. Song W, Xiao Z, Wang Y et al (2019) Session-based social recommendation via dynamic graph attention networks[C]. In: Proceedings of the Twelfth ACM international conference on web search and data mining, pp 555–563

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Funding

The work is supported by the National Natural Science Foundation of China (No.61702063), the Natural Science Foundation of Chongqing (No.cstc2019jcyj-msxmX0544), the Science and Technology Research Program of Chongqing Municipal Education Commission (No.KJQN202001136).

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Authors and Affiliations

  1. School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China

    Yihao Zhang, Meng Yuan, Chu Zhao & Mian Chen

  2. School of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China

    Xiaoyang Liu

Authors
  1. Yihao Zhang
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  2. Meng Yuan
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  3. Chu Zhao
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  4. Mian Chen
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  5. Xiaoyang Liu
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Contributions

Yihao Zhang: Conceptualization, Methodology, Software, Validation, Data Curation, Writing- Original Draft, Writing - Review and Editing.

Meng Yuan: Methodology, Formal analysis, Data Curation, Writing - Review and Editing.

Chu Zhao, Mian Chen and Xiaoyang Liu: Formal analysis, Validation, Writing- Review and Editing.

Corresponding author

Correspondence to Meng Yuan.

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This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of Interests

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Aggregating Knowledge-aware Graph Neural Network and Adaptive Relational Attention for Recommendation”.

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Zhang, Y., Yuan, M., Zhao, C. et al. Aggregating knowledge-aware graph neural network and adaptive relational attention for recommendation. Appl Intell 52, 17941–17953 (2022). https://doi.org/10.1007/s10489-022-03359-w

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