Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Multi-behavior recommendation based on intent learning

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Users often exhibit different intents when interacting with recommender systems, guiding their engagement across various behavior categories like clicks, ratings, and purchases. However, most current approaches overlook this diversity of user behaviors, making it difficult to capture the varied structural linkages occurring across multiple interaction types. Additionally, prior multi-behavior recommendation research frequently neglects modeling the underlying intents motivating different activities. Consequently, the potential of leveraging behavioral data to enhance recommendation performance for target outcomes remains underutilized. Exploring behavior intent is critical for recommender systems, but poses significant challenges due to three key factors: (1) capturing the diverse intents behind multiple interaction behaviors, (2) modeling interdependencies among various user-item interactions, and (3) integrating multi-behavior signals with heterogeneous user behavior collaboration characteristics. To address these difficulties, we propose a novel model called Multi-Behavior Knowledge Graph Intent Network (MBKGIN). MBKGIN utilizes a knowledge graph to understand the intents behind behaviors, overcoming limitations of previous methods. Specifically, MBKGIN constructs multi-behavior dependencies using a multi-head attention mechanism and incorporates intent information from the knowledge graph. Experiments on real-world datasets demonstrate MBKGIN’s effective utilization of multi-behavior data.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. Wang, H., Zhang, F., Zhang, M., Leskovec, J., Zhao, M., Li, W., Wang, Z.: 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. KDD ’19, pp. 968–977. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330836

  2. Altenburger, K.M., Ho, D.E.: Is yelp actually cleaning up the restaurant industry? a re-analysis on the relative usefulness of consumer reviews. In: The World Wide Web Conference. WWW ’19, pp. 2543–2550. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3308558.3313683

  3. Gao, C., Huang, C., Yu, Y., Wang, H., Li, Y., Jin, D.: Privacy-preserving cross-domain location recommendation. In: Proceedings of the ACM Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3(1) (2019). https://doi.org/10.1145/3314398

  4. Gu, Y., Ding, Z., Wang, S., Yin, D.: Hierarchical user profiling for e-commerce recommender systems. In: Proceedings of the 13th International Conference on Web Search and Data Mining. WSDM ’20, pp. 223–231. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3336191.3371827

  5. Huang, C., Wu, X., Zhang, X., Zhang, C., Zhao, J., Yin, D., Chawla, N.V.: Online purchase prediction via multi-scale modeling of behavior dynamics. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’19, pp. 2613–2622. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330790

  6. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009). https://doi.org/10.1109/MC.2009.263

    Article  Google Scholar 

  7. Rendle, S.: Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp. 995–1000 (2010). https://doi.org/10.1109/ICDM.2010.127

  8. Cheng, H.-T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., Anderson, G., Corrado, G., Chai, W., Ispir, M., Anil, R., Haque, Z., Hong, L., Jain, V., Liu, X., Shah, H.: Wide and deep learning for recommender systems. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. DLRS 2016, pp. 7–10. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2988450.2988454

  9. Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: Deepfm: a factorization-machine based neural network for CTR prediction. CoRR. arXiv:1703.04247 (2017)

  10. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. WWW ’17, pp. 173–182. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2017). https://doi.org/10.1145/3038912.3052569

  11. Loni, B., Pagano, R., Larson, M., Hanjalic, A.: Bayesian personalized ranking with multi-channel user feedback. In: Proceedings of the 10th ACM Conference on Recommender Systems. RecSys ’16, pp. 361–364. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2959100.2959163

  12. Qiu, H., Liu, Y., Guo, G., Sun, Z., Zhang, J., Nguyen, H.T.: Bprh: Bayesian personalized ranking for heterogeneous implicit feedback. Inf. Sci. 453, 80–98 (2018). https://doi.org/10.1016/j.ins.2018.04.027

    Article  Google Scholar 

  13. Chen, C., Zhang, M., Zhang, Y., Ma, W., Liu, Y., Ma, S.: Efficient heterogeneous collaborative filtering without negative sampling for recommendation. Proc. AAAI Conf. Artif. Intell. 34(01), 19–26 (2020). https://doi.org/10.1609/aaai.v34i01.5329

    Article  Google Scholar 

  14. Gao, C., He, X., Gan, D., Chen, X., Feng, F., Li, Y., Chua, T.-S., Yao, L., Song, Y., Jin, D.: Learning to recommend with multiple cascading behaviors. IEEE Trans. Knowl. Data Eng. 33(6), 2588–2601 (2021). https://doi.org/10.1109/TKDE.2019.2958808

    Article  Google Scholar 

  15. Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’08, pp. 650–658. Association for Computing Machinery, New York, NY, USA (2008). https://doi.org/10.1145/1401890.1401969

  16. Tang, L., Long, B., Chen, B.-C., Agarwal, D.: An empirical study on recommendation with multiple types of feedback. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 283–292. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939690

  17. Xia, L., Huang, C., Xu, Y., Dai, P., Lu, M., Bo, L.: Multi-behavior enhanced recommendation with cross-interaction collaborative relation modeling. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 1931–1936 (2021). https://doi.org/10.1109/ICDE51399.2021.00179

  18. Xia, L., Huang, C., Xu, Y., Dai, P., Zhang, X., Yang, H., Pei, J., Bo, L.: Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. Proc. AAAI Conf. Artifi. Intell. 35(5), 4486–4493 (2021). https://doi.org/10.1609/aaai.v35i5.16576

    Article  Google Scholar 

  19. Wei, W., Huang, C., Xia, L., Xu, Y., Zhao, J., Yin, D.: Contrastive meta learning with behavior multiplicity for recommendation. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. WSDM ’22, pp. 1120–1128. Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3488560.3498527

  20. Xia, L., Xu, Y., Huang, C., Dai, P., Bo, L.: Graph meta network for multi-behavior recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’21, pp. 757–766. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3404835.3462972

  21. Gu, S., Wang, X., Shi, C., Xiao, D.: Self-supervised graph neural networks for multi-behavior recommendation. In: International Joint Conference on Artificial Intelligence (IJCAI) (2022)

  22. Zhang, C., Chen, R., Zhao, X., Han, Q., Li, L.: Denoising and prompt-tuning for multi-behavior recommendation. In: Proceedings of the ACM Web Conference 2023. WWW ’23, pp. 1355–1363. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3543507.3583513

  23. Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’19, pp. 793–803. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330961

  24. Chami, I., Ying, R., Ré, C., Leskovec, J.: Hyperbolic graph convolutional neural networks. Adv. Neural. Inf. Process. Syst. 32, 4869–4880 (2019)

    Google Scholar 

  25. Gao, H., Wang, Z., Ji, S.: Large-scale learnable graph convolutional networks. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’18, pp. 1416–1424. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219947

  26. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. Adv. Neural Inf. Process. Syst. 30, 1024–1034 (2017)

    Google Scholar 

  27. Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., Yu, P.S.: Heterogeneous graph attention network. In: The World Wide Web Conference. WWW ’19, pp. 2022–2032. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3308558.3313562

  28. van den Berg, R., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv:1706.02263 (2017)

  29. Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’18, pp. 974–983. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219890

  30. Wang, X., He, X., Wang, M., Feng, F., Chua, T.-S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR’19, pp. 165–174. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3331184.3331267

  31. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: 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. SIGIR ’20, pp. 639–648. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3397271.3401063

  32. Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: The World Wide Web Conference. WWW ’19, pp. 417–426. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3308558.3313488

  33. Wang, X., He, X., Cao, Y., Liu, M., Chua, T.-S.: Kgat: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19, pp. 950–958. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330989

  34. Qiu, J., Chen, Q., Dong, Y., Zhang, J., Yang, H., Ding, M., Wang, K., Tang, J.: Gcc: graph contrastive coding for graph neural network pre-training. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’20, pp. 1150–1160. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3394486.3403168

  35. You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. CoRR. arXiv:2010.13902 (2020)

  36. Chen, M., Huang, C., Xia, L., Wei, W., Xu, Y., Luo, R.: Heterogeneous graph contrastive learning for recommendation. In: Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining. WSDM ’23, pp. 544–552. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3539597.3570484

  37. Schlichtkrull, M., Kipf, T.N., Bloem, P., Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., Navigli, R., Vidal, M.-E., Hitzler, P., Troncy, R., Hollink, L., Tordai, A., Alam, M. (eds.) The Semantic Web, pp. 593–607. Springer, Cham (2018)

    Chapter  Google Scholar 

  38. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=rJXMpikCZ. Accessed 28 Feb 2022

  39. Cui, Z., Chen, H., Cui, L., Liu, S., Liu, X., Xu, G., Yin, H.: Reinforced kgs reasoning for explainable sequential recommendation. World Wide Web 25(2), 631–654 (2022)

    Article  Google Scholar 

  40. Zhang, W., Mao, J., Cao, Y., Xu, C.: Multiplex graph neural networks for multi-behavior recommendation. In: Proceedings of the 29th ACM International Conference on Information and Knowledge Management. CIKM ’20, pp. 2313–2316. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3340531.3412119

  41. Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., Zha, H.: Beyond clicks: Modeling multi-relational item graph for session-based target behavior prediction. In: Proceedings of The Web Conference 2020. WWW ’20, pp. 3056–3062. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3366423.3380077

  42. Wang, W., Zhang, W., Liu, S., Liu, Q., Zhang, B., Lin, L., Zha, H.: Incorporating link prediction into multi-relational item graph modeling for session-based recommendation. IEEE Trans. Knowl. Data Eng. 35(3), 2683–2696 (2023). https://doi.org/10.1109/TKDE.2021.3111436

    Article  Google Scholar 

  43. Chen, C., Ma, W., Zhang, M., Wang, Z., He, X., Wang, C., Liu, Y., Ma, S.: Graph heterogeneous multi-relational recommendation. Proc. AAAI Conf. Artif. Intell. 35(5), 3958–3966 (2021). https://doi.org/10.1609/aaai.v35i5.16515

    Article  Google Scholar 

  44. Jin, B., Gao, C., He, X., Jin, D., Li, Y.: Multi-behavior recommendation with graph convolutional networks. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’20, pp. 659–668. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3397271.3401072

  45. Yang, H., Chen, H., Li, L., Yu, P.S., Xu, G.: Hyper meta-path contrastive learning for multi-behavior recommendation. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 787–796 (2021). https://doi.org/10.1109/ICDM51629.2021.00090

  46. Yu, B., Zhang, R., Chen, W., Fang, J.: Graph neural network based model for multi-behavior session-based recommendation. GeoInformatica 26(2), 429–447 (2022)

    Article  Google Scholar 

  47. Huang, J., Zhao, W.X., Dou, H., Wen, J.-R., Chang, E.Y.: Improving sequential recommendation with knowledge-enhanced memory networks. In: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR ’18, pp. 505–514. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3209978.3210017

  48. Wang, H., Zhang, F., Xie, X., Guo, M.: Dkn: deep knowledge-aware network for news recommendation. In: Proceedings of the 2018 World Wide Web Conference. WWW ’18, pp. 1835–1844. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE (2018). https://doi.org/10.1145/3178876.3186175

  49. Zhang, F., Yuan, N.J., Lian, D., Xie, X., Ma, W.-Y.: Collaborative knowledge base embedding for recommender systems. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16, pp. 353–362. Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939673

  50. Wang, X., Wang, D., Xu, C., He, X., Cao, Y., Chua, T.-S.: Explainable reasoning over knowledge graphs for recommendation. Proc. AAAI Conf. Artif. Intell. 33(01), 5329–5336 (2019). https://doi.org/10.1609/aaai.v33i01.33015329

    Article  Google Scholar 

  51. Zhao, H., Yao, Q., Li, J., Song, Y., Lee, D.L.: Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’17, pp. 635–644. Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3097983.3098063

  52. Wang, H., Zhang, F., Wang, J., Zhao, M., Li, W., Xie, X., Guo, M.: Ripplenet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. CIKM ’18, pp. 417–426. Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3269206.3271739

  53. Qu, Y., Bai, T., Zhang, W., Nie, J., Tang, J.: 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. DLP-KDD ’19. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3326937.3341257

  54. Zhao, J., Zhou, Z., Guan, Z., Zhao, W., Ning, W., Qiu, G., He, X.: Intentgc: a scalable graph convolution framework fusing heterogeneous information for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’19, pp. 2347–2357. Association for Computing Machinery, New York, NY, USA (2019). https://doi.org/10.1145/3292500.3330686

  55. Wang, X., Huang, T., Wang, D., Yuan, Y., Liu, Z., He, X., Chua, T.-S.: Learning intents behind interactions with knowledge graph for recommendation. In: Proceedings of the Web Conference 2021. WWW ’21, pp. 878–887. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3442381.3450133

  56. Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th International Conference on World Wide Web. WWW ’15 Companion, pp. 111–112. Association for Computing Machinery, New York, NY, USA (2015). https://doi.org/10.1145/2740908.2742726

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (nos. 72271024, 71871019).

Author information

Authors and Affiliations

  1. School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China

    Xinglin Pan & Mingxin Gan

Authors
  1. Xinglin Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingxin Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XP: conceptualization, methodology, data curation, software, validation, writing—original draft, writing—review. MG: conceptualization, writing—review and editing, supervision, funding acquisition.

Corresponding author

Correspondence to Mingxin Gan.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Communicated by T. Yao.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pan, X., Gan, M. Multi-behavior recommendation based on intent learning. Multimedia Systems 29, 3655–3668 (2023). https://doi.org/10.1007/s00530-023-01191-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-023-01191-x

Keywords

Search

Navigation