Skip to main content
SpringerLink
Log in

Multimodal deep collaborative filtering recommendation based on dual attention

  • S.I.: AI based Techniques and Applications for Intelligent IoT Systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The current collaborative filtering algorithm is difficult to quantify the interaction between user and item features, which makes it difficult to accurately identify user preferences. Therefore, a multimodal deep collaborative filtering recommendation model based on dual attention for crowdfunding platforms is proposed. The model first uses the dual attention mechanism to quantify investor preferences, then uses deep neural networks to learn the nonlinear interaction of item features, and then combines the collaborative filtering mechanism to model investor preferences and item features to predict the recommendation list. Meanwhile, in terms of features, a large amount of auxiliary information is used to construct a richer feature system through multimodal fusion as a way to alleviate the cold start problem and improve the prediction accuracy. The effect of hyper-parameters on the experimental performance of the real crowdfunding dataset Indiegogo is explored and baseline experiments are designed for comparison. The experimental results show that the proposed model achieves the best recommendation results on the Indiegogo dataset compared to other baseline models.

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

Access this article

Log in via an institution

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

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Meyskens M, Bird L (2015) Crowdfunding and value creation. Entrep Res J 5(2):155–166

    Google Scholar 

  2. Nassar N, Jafar A, Rahhal Y (2020) A novel deep multi-criteria collaborative filtering model for recommendation system. Knowl-Based Syst 187:1–7

    Article  Google Scholar 

  3. Fu M, Qu H, Yi Z et al (2019) A novel deep learning-based collaborative filtering model for recommendation system. IEEE Trans Cybern 49(3):1084–1096

    Article  Google Scholar 

  4. Chen Y (2019) Research on personalized recommendation algorithm based on user preference in mobile e-commerce. Inf Syst E-Bus Manage. https://doi.org/10.1007/s10257-019-00401-2

    Article  Google Scholar 

  5. Hu Y, Fan X, Zhang R et al (2015) Context-aware web services recommendation based on user preference expansion. In: APSCC 2015: advances in services computing, pp 108–120

  6. Xu LB, Li XS, Guo Y (2019) Gauss-core extension dependent prediction algorithm for collaborative filtering recommendation. Cluster Comput 22(4):11501–11511

    Article  Google Scholar 

  7. Yuan Z, Lee JH, Zhang S (2021) Optimization of the hybrid movie recommendation system based on weighted classification and user collaborative filtering algorithm. Complexity 2021:1–13

    Google Scholar 

  8. Wu ZW, Chen CT, Huang SH (2022) Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning. Neural Comput Appl 34:3097–3115

    Article  Google Scholar 

  9. Wang F, Wen Y, Guo T, et al. Collaborative filtering and association rule mining‐based market basket recommendation on spark. Concurrency and Computation: Practice and Experience, 2020, 32(7).

  10. Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1235–1244

  11. Qu Y, Han C, Kan R et al (2016) Product-based neural networks for user response prediction. In: Proceedings of the 16th international conference on data mining, pp 1149–1154

  12. Guo H, Tang R, Ye Y et al (2017) DeepFM: a factorization-machine based neural network for CTR prediction. In: Proceedings of the 26th international joint conference on artificial intelligence

  13. Lara-Cabrera R, González-prieto Á, Ortega F (2020) Deep matrix factorization approach for collaborative filtering recommender systems. Appl Sci 10(14):4926

    Article  Google Scholar 

  14. Grbovic M, Cheng H (2018) Real-time personalization using embeddings for search ranking at airbnb. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 311–320

  15. Luo Y, Peng J, Ma J (2020) When causal inference meets deep learning. Nat Mach Intell 2(8):426–427

    Article  Google Scholar 

  16. Bahdanau D, Cho KH, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proceedings of the 3rd international conference on learning representations

  17. Liu G, Zhang L, Wu J et al (2021) Recommendation with attribute-aware product networks: a representation learning model. In: Proceedings of the 17th ACM conference

  18. Xiao J, Ye H, He X et al (2017) Attentional factorization machines: learning the weight of feature interactions via attention networks. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 3119–3125

  19. Rendle S (2010) Factorization machines. In: Proceedings of the 10th international conference on data mining, pp 995–1000

  20. Zhou G, Zhu X, Song C et al (2018) Deep interest network for click-through rate prediction. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1059–1068

  21. Zhou G, Mou N, Fan Y et al (2019) Deep interest evolution network for click-through rate prediction. In: Proceedings of the AAAI conference on artificial intelligence, pp 5941–5948

  22. Song W, Shi C, Xiao Z et al (2019) AutoInt: automatic feature interaction learning via self-attentive neural networks. Proceedings of the 28th ACM international conference on information and knowledge management, pp 1161–1170

  23. He X, He Z, Song J et al (2018) NAIS: neural attentive item similarity model for recommendation. IEEE Trans Knowl Data Eng 30(12):2354–2366

    Article  Google Scholar 

  24. Fan J, Jiang YC, Liu YZ et al (2021) Interpretable MOOC recommendation: a multi-attention network for personalized learning behavior analysis. Internet Res. https://doi.org/10.1108/INTR-08-2020-0477

    Article  Google Scholar 

  25. Song W, Xiao Z, Wang Y et al (2019) Session-based social recommendation via dynamic graph attention networks. In: Proceedings of the 12th ACM international conference on web search and data mining, pp 555–563

  26. Liu Y, Tian Z, Sun J et al (2020) Distributed representation learning via node2vec for implicit feedback recommendation. Neural Comput Appl 32:4335–4345

    Article  Google Scholar 

  27. Wang H, Chen S (2020) A Bipartite graph-based recommender for crowdfunding with sparse data. In: Haron R, Husin MM, Murg M (eds) Banking and finance. IntechOpen, London

    Google Scholar 

  28. Song Y, Li Z, Sahoo N (2020) Matching returning donors to projects on philanthropic crowdfunding platform. Manage Sci 68(1):355–375

    Article  Google Scholar 

  29. Rakesh V, Lee W-C, Reddy CK (2016) Probabilistic group recommendation model for crowdfunding domains. In: Proceedings of the ninth ACM international conference on web search and data mining, pp 257–266

  30. Zeyu R, Zhenchao W, Zunwang K et al (2021) A review of multimodal data fusion. Comput Eng Appl 57(18):49–64

    Google Scholar 

  31. Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: Proceedings of the 31st international conference on international conference on machine learning, pp 1188–1196

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

  33. Xue H-J, Dai X-Y, Zhang J et al (2017) Deep matrix factorization models for recommender systems. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 3203–3209

  34. Mcmahan HB, Holt G, Sculley D et al (2013) Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1222–1230

  35. He X, Chua T-S (2017) Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, pp 355–364

  36. Fan Z, Liu Z, Wang Y et al (2022) Sequential recommendation via stochastic self-attention. In: Proceedings of the ACM web conference 2022. Virtual Event, Lyon, France; Association for Computing Machinery, pp 2036–2047

  37. Kang WC, Mcauley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE international conference on data mining (ICDM)

  38. Hinton GE (2008) Visualizing High-dimensional data using t-SNE. Vigiliae Christianae 9:2579–2605

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 71771177, 71871143). The financial support is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Pei Yin, Dandan Ji, Han Yan, Hongcheng Gan & Jinxian Zhang

Authors
  1. Pei Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dandan Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Han Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongcheng Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinxian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pei Yin.

Ethics declarations

Competing interests

The authors declare that there is no conflict of interest with any financial organizations regarding the material reported in this manuscript.

Additional information

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 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

Yin, P., Ji, D., Yan, H. et al. Multimodal deep collaborative filtering recommendation based on dual attention. Neural Comput & Applic 35, 8693–8706 (2023). https://doi.org/10.1007/s00521-022-07756-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-07756-7

Keywords

Search

Navigation