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Cross attention fusion for knowledge graph optimized recommendation

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Abstract

Knowledge Graph has attracted a wide range of attention in the field of recommendation, which is usually applied as auxiliary information to solve the problem of data sparsity. However, most recommendation models cannot effectively mine the associations between the items to be recommended and the entities in the Knowledge Graph. In this paper, we propose CAKR, a knowledge graph recommendation method based on the cross attention unit, which is similar to MKR, a multi-task feature learning general framework that uses knowledge graph embedding tasks to assist recommendation tasks. Specifically, we design a new method to optimize the feature interaction between the items and the corresponding entities in the Knowledge Graph and propose a feature cross-unit combined with the attention mechanism to enhance the recommendation effect. Through extensive experiments on the public datasets of movies, books, and music, we prove that CAKR is better than MKR and other knowledge graph recommendation methods so that the new feature cross-unit designed in this paper is effective in improving the accuracy of the recommendation system.

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References

  1. Bordes A, Usunier N, Garcia-Duran A, Weston J, Yakhnenko O (2013) Translating embeddings for modeling multi-relational data. In: Neural information processing systems (NIPS), pp 1–9

  2. Cao Y, Wang X, He X, hu Z, Chua T S (2019) Unifying knowledge graph learning and recommendation: Towards a better understanding of user preferences. World Wide Web Conf, pp 151–161. https://doi.org/10.1145/3308558.3313705

  3. Guo Q, Zhuang F, Qin C, Zhu H, Xie X, Xiong H, He Q (2020) A survey on knowledge graph-based recommender systems. IEEE Trans Knowl Data Eng

  4. Ji G, He S, Xu L, Liu K, Zhao J (2015) Knowledge graph embedding via dynamic mapping matrix. 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

  5. Lin Y, Liu Z, Sun M, Liu Y, Zhu X (2015) Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI conference on artificial intelligence, vol 29

  6. Pan Y, He F, Yu H (2020a) A correlative denoising autoencoder to model social influence for top-N recommender system. Front Comput Sci 14(3):143301. https://doi.org/10.1007/s11704-019-8123-3

    Article  Google Scholar 

  7. Pan Y, He F, Yu H (2020b) Learning social representations with deep autoencoder for recommender system. World Wide Web 23(4):2259–2279. https://doi.org/10.1007/s11280-020-00793-z

    Article  Google Scholar 

  8. Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: European semantic web conference. Springer, pp 593–607

  9. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  10. Sun Z, Yang J, Zhang J, Bozzon A, Huang LK, Xu C (2018) Recurrent knowledge graph embedding for effective recommendation. In: Proceedings of the 12th ACM conference on recommender systems, pp 297–305

  11. Sun Z, Deng ZH, Nie JY, Tang J (2019) Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv:190210197

  12. Tang H, Zhao G, Bu X, Qian X (2021) Dynamic evolution of multi-graph based collaborative filtering for recommendation systems. Knowl-Based Syst 228:107251. https://doi.org/10.1016/j.knosys.2021.107251

    Article  Google Scholar 

  13. Trouillon T, Welbl J, Riedel S, Gaussier R, Bouchard G (2016) Complex embeddings for simple link prediction. In: International conference on machine learning, PMLR, pp 2071–2080

  14. Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018a) Ripplenet: Propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 417–426

  15. Wang H, Zhang F, Xie X, Guo M (2018b) Dkn: Deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 world wide web conference, pp 1835–1844

  16. Wang H, Zhang F, Zhao M, Li W, Xie X, Guo M (2019a) Multi-task feature learning for knowledge graph enhanced recommendation. In: The world wide web conference, pp 2000–2010

  17. Wang Q, Mao Z, Wang B, Guo L (2017) Knowledge graph embedding: A survey of approaches and applications. IEEE Trans Knowl Data Eng 29(12):2724–2743

    Article  Google Scholar 

  18. Wang X, He X, Cao Y, Liu M, Chua TS (2019b) KGAT: Knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. pp 950–958 https://doi.org/10.1145/3292500.3330989. arXiv:1905.07854

  19. Wang X, Wang D, Xu C, He X, Cao Y, Chua T S (2019c) Explainable reasoning over knowledge graphs for recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5329–5336

  20. Wang Y, Dong L, Li Y, Zhang H (2021) Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet. PLOS ONE 16(5):e0251162. https://doi.org/10.1371/journal.pone.0251162, publisher: Public Library of Science

    Article  Google Scholar 

  21. Wu Y, Li K, Zhao G, Qian X (2020) Personalized long- and short-term preference learning for next POI recommendation. IEEE Trans Knowl Data Eng 1–1. https://doi.org/10.1109/TKDE.2020.3002531

  22. Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J (2014) Personalized entity recommendation: A heterogeneous information network approach. In: Proceedings of the 7th ACM international conference on Web search and data mining, pp 283–292

  23. Zhang F, Yuan NJ, Lian D, Xie X, Ma WY (2016) Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 353–362

  24. Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning, based recommender system: A survey and new perspectives. ACM Comput Surveys (CSUR) 52(1):1–38

    Article  Google Scholar 

  25. Zhang Y, Yang Q (2021) A survey on multi-task learning. IEEE Trans Knowl Data Eng

  26. Zhao G, Lei X, Qian X, Mei T (2018) Exploring users’ internal influence from reviews for social recommendation. IEEE Trans Multimedia 21(3):771–781. https://doi.org/10.1109/TMM.2018.2863598

    Article  Google Scholar 

  27. Zhong H, Zhang J, Wang Z, Wan H, Chen Z (2015) Aligning knowledge and text embeddings by entity descriptions. In: Proceedings of the 2015 conference on empirical methods in natural language processing, pp 267–272

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Hebei University of Engineering, Handan, 056038, China

    Weijian Huang, Jianhua Wu, Weihu Song & Zehua Wang

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  1. Weijian Huang
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  2. Jianhua Wu
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  3. Weihu Song
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  4. Zehua Wang
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Correspondence to Jianhua Wu.

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Huang, W., Wu, J., Song, W. et al. Cross attention fusion for knowledge graph optimized recommendation. Appl Intell 52, 10297–10306 (2022). https://doi.org/10.1007/s10489-021-02930-1

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