Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Intent-Aware Graph-Level Embedding Learning Based Recommendation

  • Regular Paper
  • Published:
Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

Recommendation has been widely used in business scenarios to provide users with personalized and accurate item lists by efficiently analyzing complex user-item interactions. However, existing recommendation methods have significant shortcomings in capturing the dynamic preference changes of users and discovering their true potential intents. To address these problems, a novel framework named Intent-Aware Graph-Level Embedding Learning (IaGEL) is proposed for recommendation. In this framework, the potential user interest is explored by capturing the co-occurrence of items in different periods, and then user interest is further improved based on an adaptive aggregation algorithm, forming generic intents and specific intents. In addition, for better representing the intents, graph-level embedding learning is designed based on the mutual information comparison among positive intents and negative intents. Finally, an intent-based recommendation strategy is designed to further mine the dynamic changes in user preferences. Experiments on three public and industrial datasets demonstrate the effectiveness of the proposed IaGEL in the task of recommendation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Yang M, Li Z, Zhou M, Liu J, Irwin K. HICf: Hyperbolic informative collaborative filtering. In Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2022, pp.2212–2221. DOI: https://doi.org/10.1145/3534678.3539475.

    Chapter  Google Scholar 

  2. Xia L, Huang C, Zhang C. Self-supervised hypergraph transformer for recommender systems. In Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2022, pp.2100–2109. DOI: https://doi.org/10.1145/3534678.3539473.

    Chapter  Google Scholar 

  3. Dong Y, Chawla N V, Swami A. Metapath2vec: Scalable representation learning for heterogeneous networks. In Proc. the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2017, pp.135–144. DOI: https://doi.org/10.1145/3097983.3098036.

    Google Scholar 

  4. Fu T Y, Lee W C, Lei Z. HIN2Vec: Explore meta-paths in heterogeneous information networks for representation learning. In Proc. the 2017 ACM Conference on Information and Knowledge Management, Nov. 2017, pp.1797–1806. DOI: https://doi.org/10.1145/3132847.3132953.

    Chapter  Google Scholar 

  5. Feng Y, Lv F, Hu B, Sun F, Kuang K, Liu Y, Liu Q, Ou W. MTBRN: Multiplex target-behavior relation enhanced network for click-through rate prediction. In Proc. the 29th ACM International Conference on Information & Knowledge Management, Oct. 2020, pp.2421–2428. DOI: https://doi.org/10.1145/3340531.3412729.

    Google Scholar 

  6. Wu K, Bian W, Chan Z, Ren L, Xiang S, Han S G, Deng H, Zheng B. Adversarial gradient driven exploration for deep click-through rate prediction. In Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2022, pp.2050–2058. DOI:https://doi.org/10.1145/3534678.3539461.

    Chapter  Google Scholar 

  7. Wang G, Zhong T, Xu X, Zhang K, Zhou F, Wang Y. Vector-quantized autoencoder with copula for collaborative filtering. In Proc. the 30th ACM International Conference on Information & Knowledge Management, Nov. 2021, pp.3458–3462. DOI: https://doi.org/10.1145/3459637.3482216.

    Google Scholar 

  8. He X, Liao L, Zhang H, Nie L, Hu X, Chua T S. Neural collaborative filtering. In Proc. the 26th International Conference on World Wide Web, Apr. 2017, pp.173–182. DOI: https://doi.org/10.1145/3038912.3052569.

    Chapter  Google Scholar 

  9. Wang Z, Zhao H, Shi C. Profiling the design space for graph neural networks based collaborative filtering. In Proc. the 15th ACM International Conference on Web Search and Data Mining, Feb. 2022, pp.1109–1119. DOI: https://doi.org/10.1145/3488560.3498520.

    Google Scholar 

  10. Gong J, Wang S, Wang J, Feng W, Hao P, Tang J, Yu P S. Attentional graph convolutional networks for knowledge concept recommendation in MOOCs in a heterogeneous view. In Proc. the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2020, pp.79–88. DOI: https://doi.org/10.1145/3397271.3401057.

    Google Scholar 

  11. Yang Y, Guan Z, Li J, Zhao W, Cui J, Wang Q. Interpretable and efficient heterogeneous graph convolutional network. IEEE Trans. Knowledge and Data Engineering, 2023, 35(2): 1637–1650. DOI: https://doi.org/10.1109/TKDE.2021.3101356.

    Google Scholar 

  12. Fu X, Zhang J, Meng Z, King I. MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proc. the 29th International Conference on World Wide Web, Apr. 2020, pp.2331–2341. DOI: https://doi.org/10.1145/3366423.3380297.

    Google Scholar 

  13. Hao P, Qian Z, Wang S, Bai C. Community aware graph embedding learning for item recommendation. World Wide Web, 2023, 26(6): 4093–4108. DOI: https://doi.org/10.1007/s11280-023-01224-5.

    Article  Google Scholar 

  14. Li F, Chen Z, Wang P, Ren Y, Zhang D, Zhu X. Graph intention network for click-through rate prediction in sponsored search. In Proc. the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2019, pp.961–964. DOI: https://doi.org/10.1145/3331184.3331283.

    Google Scholar 

  15. Jiang W, Jiao Y, Wang Q, Liang C, Guo L, Zhang Y, Sun Z, Xiong Y, Zhu Y. Triangle graph interest network for click-through rate prediction. In Proc. the 15th ACM International Conference on Web Search and Data Mining, Feb. 2022, pp.401–409. DOI: https://doi.org/10.1145/3488560.3498458.

    Google Scholar 

  16. Chen T, Yin H, Chen H, Yan R, Nguyen Q V H, Li X. AIR: Attentional intention-aware recommender systems. In Proc. the 35th IEEE International Conference on Data Engineering, Apr. 2019, pp.304–315. DOI: https://doi.org/10.1109/ICDE.2019.00035.

    Google Scholar 

  17. Yang Y, Huang C, Xia L, Liang Y, Yu Y, Li C. Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In Proc. the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Aug. 2022, pp.2263–2274. DOI: https://doi.org/10.1145/3534678.3539342.

    Chapter  Google Scholar 

  18. Wang R, Yang N, Yu P S. Learning aspect-level complementarity for intent-aware complementary recommendation. Knowledge-Based Systems, 2022, 258:109936. DOI: https://doi.org/10.1016/j.knosys.2022.109936.

    Article  Google Scholar 

  19. Guo J, Yang Y, Song X, Zhang Y, Wang Y, Bai J, Zhang Y. Learning multi-granularity consecutive user intent unit for session-based recommendation. In Proc. the 15th ACM International Conference on Web Search and Data Mining, Feb. 2022, pp.343–352. DOI: https://doi.org/10.1145/3488560.3498524.

    Google Scholar 

  20. Fan S, Zhu J, Han X, Shi C, Hu L, Ma B, Li Y. Metapath-guided heterogeneous graph neural network for intent recommendation. In Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2019, pp.2478–2486. DOI: https://doi.org/10.1145/3292500.3330673.

    Chapter  Google Scholar 

  21. Li J, Sun P, Wang Z, Ma W, Li Y, Zhang M, Feng Z. Intent-aware ranking ensemble for personalized recommendation. In Proc. the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2023, pp.1004–1013. DOI: https://doi.org/10.1145/3539618.3591702.

    Google Scholar 

  22. Perozzi B, Al-Rfou R, Skiena S. DeepWalk: Online learning of social representations. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2014, pp.701–710. DOI: https://doi.org/10.1145/2623330.2623732.

    Chapter  Google Scholar 

  23. Grover A, Leskovec J. Node2vec: Scalable feature learning for networks. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2016, pp.855–864. DOI: https://doi.org/10.1145/2939672.2939754.

    Chapter  Google Scholar 

  24. Wang J, Huang P, Zhao H, Zhang Z, Zhao B, Lee D L. Billion-scale commodity embedding for E-commerce recommendation in Alibaba. In Proc. the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Jul. 2018, pp.839–848. DOI: https://doi.org/10.1145/3219819.3219869.

    Chapter  Google Scholar 

  25. Kipf T N, Welling M. Variational graph auto-encoders. arXiv: 1611.07308, 2016. https://arxiv.org/abs/1611.07308, Sept. 2024.

    Google Scholar 

  26. Velickovic P, Fedus W, Hamilton W L, Liò P, Bengio Y, Hjelm R D. Deep graph infomax. In Proc. the 7th International Conference on Learning Representations, May 2019.

    Google Scholar 

  27. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In Proc. the 5th International Conference on Learning Representations, Apr. 2017.

    Google Scholar 

  28. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. arXiv: 1710.10903, 2017. https://arxiv.org/abs/1710.10903, Sept. 2024.

    Google Scholar 

  29. Hamilton W L, Ying Z, Leskovec J. Inductive representation learning on large graphs. In Proc. the 31st International Conference on Neural Information Processing Systems, Dec. 2017. DOI: https://doi.org/10.5555/3294771.3294869.

    Google Scholar 

  30. Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? In Proc. the 7th International Conference on Learning Representations, May 2019.

    Google Scholar 

  31. Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In Proc. the 1st International Conference on Learning Representations, May 2013.

    Google Scholar 

  32. Page L, Brin S, Motwani R, Winograd T. The PageRank citation ranking: Bringing order to the web. Technical Report, Stanford InfoLab, 1999. http://ilpubs.stanford.edu:8090/422/, Sept. 2024.

    Google Scholar 

  33. Park N, Kan A, Dong X L, Zhao T, Faloutsos C. Estimating node importance in knowledge graphs using graph neural networks. In Proc. the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Aug. 2019, pp.596–606. DOI: https://doi.org/10.1145/3292500.3330855.

    Chapter  Google Scholar 

  34. Liu Y, Wang Q, Wang X, Zhang F, Geng L, Wu J, Xiao Z. Community enhanced graph convolutional networks. Pattern Recognition Letters, 2020, 138:462–468. DOI: https://doi.org/10.1016/j.patrec.2020.08.015.

    Article  Google Scholar 

  35. Newman M E J. Modularity and community structure in networks. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(23): 8577–8582. DOI: https://doi.org/10.1073/pnas.0601602103.

    Article  Google Scholar 

  36. Wang X, He X, Wang M, Feng F, Chua T S. Neural graph collaborative filtering. In Proc. the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2019, pp.165–174. DOI: https://doi.org/10.1145/3331184.3331267.

    Google Scholar 

  37. Fan Z, Xu K, Dong Z, Peng H, Zhang J, Yu P S. Graph collaborative signals denoising and augmentation for recommendation. In Proc. the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 2023, pp.2037–2041. DOI: https://doi.org/10.1145/3539618.3591994.

    Google Scholar 

  38. Wang Z, Liu H, Wei W, Hu Y, Mao X L, He S, Fang R, Chen D. Multi-level contrastive learning framework for sequential recommendation. In Proc. the 31st ACM International Conference on Information & Knowledge Management, Oct. 2022, pp.2098–2107. DOI: https://doi.org/10.1145/3511808.3557404.

    Google Scholar 

  39. Sun J, Zhang Y, Ma C, Coates M, Guo H, Tang R, He X. Multi-graph convolution collaborative filtering. In Proc. the 2019 IEEE International Conference on Data Mining, Nov. 2019, pp.1306–1311. DOI: https://doi.org/10.1109/ICDM.2019.00165.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310012, China

    Peng-Yi Hao  (郝鹏翼), Si-Hao Liu  (刘思浩) & Cong Bai  (白 琮)

Authors
  1. Peng-Yi Hao  (郝鹏翼)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Si-Hao Liu  (刘思浩)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cong Bai  (白 琮)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cong Bai  (白 琮).

Ethics declarations

Conflict of Interest The authors declare that they have no conflict of interest.

Additional information

Recommended by ChinaMM 2023

This work was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LR21F020002 and the National Natural Science Foundation of China under Grant No. 61976192.

Peng-Yi Hao received her Ph.D. degree in computer science from Graduate School of Information, Production and Systems, Waseda University, Tokyo, in 2013. She is currently an associate professor with the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou. Her current research interests include multimedia analysis and graph neural networks.

Si-Hao Liu is currently an undergraduate student of the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou. His research interests include data mining and graph neural networks.

Cong Bai received his B.E. degree from Shandong University, Jinan, in 2003, his M.E. degree from Shanghai University, Shanghai, in 2009, and his Ph.D. degree from the National Institute of Applied Sciences, Rennes, in 2013. He is currently a professor with the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou. His research interests include computer vision and multimedia processing.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hao, PY., Liu, SH. & Bai, C. Intent-Aware Graph-Level Embedding Learning Based Recommendation. J. Comput. Sci. Technol. 39, 1138–1152 (2024). https://doi.org/10.1007/s11390-024-3522-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-024-3522-9

Keywords

Search

Navigation