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Multimodal deep collaborative filtering recommendation based on dual attention

  • S.I.: AI based Techniques and Applications for Intelligent IoT Systems
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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.

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

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Acknowledgements

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

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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

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  1. Pei Yin
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  2. Dandan Ji
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  3. Han Yan
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  4. Hongcheng Gan
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Correspondence to Pei Yin.

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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

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