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KAT: knowledge-aware attentive recommendation model integrating two-terminal neighbor features

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Abstract

Due to its ability to effectively address the cold start and sparsity problems in collaborative filtering, knowledge graph is commonly used as auxiliary information in recommendation systems. However, the existing recommendation algorithms based on knowledge graphs mainly focus on utilizing the connection structure to obtain user interests or item features, without emphasizing the simultaneous feature extraction on both the user and item sides. Therefore, the learned embeddings can not effectively represent the potential semantics of users and items. In this paper, we proposed KAT, a knowledge-aware attentive recommendation model integrating two-terminal neighbor features, which to extract fine-grained user and item features by alternating preference propagation and neighborhood information aggregation. The two modules automatically update and share entity embedding. Specifically, we introduce knowledge-aware attention mechanism to enhance the distinction of adjacent entities. Furthermore, we design a neighbor sampling mechanism to calculate the maximum node influence by extracting the largest connected subnet, which avoids the instability of the model performance caused by random sampling. We validate the effectiveness of KAT on four different datasets: movie, music, book, and grape (the latter is a dataset that we constructed through market research). Numerous experiments have demonstrated that KAT significantly outperforms several recent baselines, and AUC and ACC have increased by 2.81% and 1.28% respectively on our self-built dataset.

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Data availability

The datasets generated and analysed during the current study are available in the MovieLens, Last.FM, Book-Crossing repository, https://grouplens.org/datasets/movielens/, https://grouplens.org/datasets/hetrec-2011/, and http://www2.informatik.uni-freiburg.de/~cziegler/BX/. Dataset Grape is not publicly available because it is built through our own research.

References

  1. Zheng G, Zhang F, Zheng Z, Xiang Y, Yuan NJ, Xie X, Li Z (2018) DRN: a deep reinforcement learning framework for news recommendation. In: Proceedings of the 2018 World Wide Web conference. WWW ’18. International World Wide Web conferences steering committee, Republic and Canton of Geneva, CHE, pp 167–176. https://doi.org/10.1145/3178876.3185994

  2. Zhou G, Zhu X, Song C, Fan Y, Zhu H, Ma X, Yan Y, Jin J, Li H, Gai K (2018) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1059–1068

  3. Wu S, Sun F, Zhang W, Xie X, Cui B (2022) Graph neural networks in recommender systems: a survey. ACM Comput Surv 55(5):1–37

    Article  Google Scholar 

  4. Feng J, Feng X, Deng L, Peng J (2018) Recommending multimedia information in a virtual Han Chang’an city roaming system. Presence 26(03):322–336

    Article  Google Scholar 

  5. Li S, Cheng X, Su S, Sun H (2017) Exploiting organizer influence and geographical preference for new event recommendation. Expert Syst 34(2):12190

    Article  Google Scholar 

  6. Lin G, Xie X, Lv Z (2016) Taobao practices, everyday life and emerging hybrid rurality in contemporary china. J Rural Stud 47:514–523

    Article  Google Scholar 

  7. Chiang J-H, Ma C-Y, Wang C-S, Hao P-Y (2022) An adaptive, context-aware, and stacked attention network-based recommendation system to capture users’ temporal preference. IEEE Trans Knowl Data Eng 35(4):3404–3418

    Article  Google Scholar 

  8. Zhang J, Yang J, Wang L, Jiang Y, Qian P, Liu Y (2021) A novel collaborative filtering algorithm and its application for recommendations in e-commerce. CMES-Comput Model Eng Sci 126(1):1–17

  9. Afsar MM, Crump T, Far B (2022) Reinforcement learning based recommender systems: a survey. ACM Comput Surv 55(7):1–38

    Article  Google Scholar 

  10. Gazdar A, Hidri L (2020) A new similarity measure for collaborative filtering based recommender systems. Knowl Based Syst 188:105058

    Article  Google Scholar 

  11. Song HS, Kim YA (2021) A dog food recommendation system based on nutrient suitability. Expert Syst 38(2):12623

    Article  Google Scholar 

  12. Hwang S, Ahn H, Park E (2023) iMovieRec: a hybrid movie recommendation method based on a user-image-item model. Int J Mach Learn Cybern 14(9):3205–3216

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

    Article  Google Scholar 

  14. Martins GB, Papa JP, Adeli H (2020) Deep learning techniques for recommender systems based on collaborative filtering. Expert Syst 37(6):12647

    Article  Google Scholar 

  15. Karydi E, Margaritis K (2016) Parallel and distributed collaborative filtering: a survey. ACM Computing Surveys (CSUR), vol 49, pp 1-41

  16. Kumar NP, Fan Z (2015) Hybrid user-item based collaborative filtering. Procedia Comput Sci 60(1):1453–1461

    Article  Google Scholar 

  17. Sharma R, Gopalani D, Meena Y (2023) An anatomization of research paper recommender system: overview, approaches and challenges. Eng Appl Artif Intell 118:105641

    Article  Google Scholar 

  18. Liu G, Zhang L, Wu J (2021) Beyond similarity: relation-based collaborative filtering. IEEE Trans Knowl Data Eng 35(1):128–140

    Google Scholar 

  19. Wang H, Zhang F, Hou M, Xie X, Guo M, Liu Q (2018) Shine: signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 592–600

  20. Nazari A, Kordabadi M, Mansoorizadeh M (2023) Scalable and data-independent multi-agent recommender system using social networks analysis. Int J Inf Technol Decis Mak 23(02):741–762

  21. Liu W, Wan H, Yan B (2023) Short video recommendation algorithm incorporating temporal contextual information and user context. CMES Comput Model Eng Sci 135(1):239–258

    Google Scholar 

  22. 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. In: The World Wide Web conference, pp 151–161

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

  24. Wang Y, Dong L, Li Y, Zhang H (2021) Multitask feature learning approach for knowledge graph enhanced recommendations with RippleNet. PLoS One 16(5):0251162

    Article  Google Scholar 

  25. Yin G, Chen F, Dong Y, Li G (2022) Knowledge-aware recommendation model with dynamic co-attention and attribute regularize. Appl Intell 52:3807–3824

  26. 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 34(8):3549–3568

    Article  Google Scholar 

  27. Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M (2018) 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

  28. Wang Z, Lin G, Tan H, Chen Q, Liu X (2020) CKAN: collaborative knowledge-aware attentive network for recommender systems. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 219–228

  29. Jiang N, Hu Z, Wen J, Zhao J, Gu W, Tu Z, Liu X, Li Y, Gong J, Lin F (2023) NAH: neighbor-aware attention-based heterogeneous relation network model in E-commerce recommendation. In: World Wide Web, vol 25, pp 2373–2394

  30. Dongliang Z, Yi W, Zichen W (2022) Review of recommendation systems based on knowledge graph. Data Anal Knowl Discov 5(12):1–13

    Google Scholar 

  31. Wang H, Zhao M, Xie X, Li W, Guo M (2019) Knowledge graph convolutional networks for recommender systems. In: The World Wide Web conference, pp 3307–3313

  32. Wang H, Zhang F, Zhang M, Leskovec J, Zhao M, Li W, Wang Z (2019) Knowledge-aware graph neural networks with label smoothness regularization for recommender systems. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 968–977

  33. Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 165–174

  34. He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp 639–648

  35. Wang X, Huang T, Wang D, Yuan Y, Liu Z, He X, Chua T-S (2021) Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the web conference 2021, pp 878–887

  36. Liu Z, Li X, Fan Z, Guo S, Achan K, Philip SY (2020) Basket recommendation with multi-intent translation graph neural network. In: 2020 IEEE international conference on Big Data (Big Data). IEEE, pp 728–737

  37. Yang Z, Cheng J (2021) Recommendation algorithm based on knowledge graph to propagate user preference. Int J Comput Int Sys 1–33

  38. Liang S, Tu H, Wang R, Yuan F, Zhang X (2021) Knowledge graph recommendation algorithm combining importance sampling and pooling aggregation. J Chin Comput Syst 42(5):967–971

    Google Scholar 

  39. Shi C (2020) Research on improved RippleNet recommendation method. Master’s thesis, Huazhong University of Science and Technology

  40. Li X, Liu Z, Guo S, Liu Z, Peng H, Philip SY, Achan K (2021) Pre-training recommender systems via reinforced attentive multi-relational graph neural network. In: 2021 IEEE international conference on Big Data (Big Data). IEEE, pp 457–468

  41. Zhang F, Yuan NJ, Lian D, Xie X, Ma W-Y (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

  42. 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, pp 2181–2187

  43. Wang H, Zhang F, Xie X, Guo M (2018) DKN: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World Wide Web conference, pp 1835–1844

  44. 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, vol 1, pp 687–696

  45. Zhang Y, Ai Q, Chen X, Wang P (2018) Learning over knowledge-base embeddings for recommendation. arXiv preprint. arXiv:1803.06540

  46. Ai Q, Azizi V, Chen X, Zhang Y (2018) Learning heterogeneous knowledge base embeddings for explainable recommendation. Algorithms 11(9):137

    Article  MathSciNet  Google Scholar 

  47. Yu X, Ren X, Gu Q, Sun Y, Han J (2013) Collaborative filtering with entity similarity regularization in heterogeneous information networks. In: IJCAI HINA 27, pp 1–6

  48. Luo C, Pang W, Wang Z, Lin C (2014) Hete-CF: social-based collaborative filtering recommendation using heterogeneous relations. In: 2014 IEEE international conference on data mining. IEEE, pp 917–922

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

  50. Qu Y, Bai T, Zhang W, Nie J, Tang J (2019) An end-to-end neighborhood-based interaction model for knowledge-enhanced recommendation. In: Proceedings of the 1st international workshop on deep learning practice for high-dimensional sparse data, pp 1–9

  51. Sha X, Sun Z, Zhang J (2021) Hierarchical attentive knowledge graph embedding for personalized recommendation. Electron Commer Res Appl 48:101071

    Article  Google Scholar 

  52. Ding L, Sun B, Shi P (2019) Empirical study of knowledge network based on complex network theory. Acta Phys Sin 68(12):324

    Article  Google Scholar 

  53. Wang X, He X, Cao Y, Liu M, Chua T-S (2019) KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp 950–958

  54. Du Y, Zhu X, Chen L, Fang Z, Gao Y (2022) MetaKG: meta-learning on knowledge graph for cold-start recommendation. arXiv:2202.03851

  55. Yang Y, Huang C, Xia L, Li C (2022) Knowledge graph contrastive learning for recommendation. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 1434–1443

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Acknowledgements

This work was supported by the China Agricultural Research System of MOF and MARA (CARS-29), and the open funds of the Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, PR China.

Funding

This work was supported by the China Agricultural Research System of MOF and MARA(CARS-29), and the open funds of the Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, PR China.

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Author notes

  1. Tianqi Liu, Xinxin Zhang and Wenzheng Wang contributed equally to this work.

Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Tsinghua East Road, Beijing, 100083, China

    Tianqi Liu, Xinxin Zhang, Wenzheng Wang & Weisong Mu

  2. The Key Laboratory of Viticulture and Enology, Ministry of Agriculture and Rural Affairs, Tsinghua East Road, Beijing, 100083, China

    Weisong Mu

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  1. Tianqi Liu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tianqi Liu, Xinxin Zhang Wenzheng Wang and Weisong Mu. The first draft of the manuscript was written by Tianqi Liu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Weisong Mu.

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All authors have read the final manuscript and approved their submission to International Journal of Machine Learning and Cybernetics. They certify that they have participated sufficiently in the work to take public responsibility for the appropriateness of the experimental design and method, and the collection, analysis, and interpretation of the data. The authors declare that they have no conflict of interest.

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Liu, T., Zhang, X., Wang, W. et al. KAT: knowledge-aware attentive recommendation model integrating two-terminal neighbor features. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02194-4

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