Multimodal deep collaborative filtering recommendation based on dual attention

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...

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Bibliographic Details
Published in:Neural computing & applications 2023-04, Vol.35 (12), p.8693-8706
Main Authors: Yin, Pei, Ji, Dandan, Yan, Han, Gan, Hongcheng, Zhang, Jinxian
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Artificial neural networks
Collaboration
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Crowdfunding
Data Mining and Knowledge Discovery
Datasets
Filtration
Funding
Image Processing and Computer Vision
Probability and Statistics in Computer Science
S.I.: AI based Techniques and Applications for Intelligent IoT Systems
Special Issue on Artificial Intelligence based Techniques and Applications for Intelligent IoT Systems (AI-TAIoT)
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Summary: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.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07756-7