Multimodal deep collaborative filtering recommendation based on dual attention

Yin, Pei; Ji, Dandan; Han, Yaling; Gan, Hongcheng; Zhang, Jinxian

doi:10.1007/s00521-022-07756-7

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…Guo et al (2023) proposed a recommendation method based on multi-factor random walk (MFRW), where MFRW calculated the current user's comprehensive trust value toward other users based on common friends, enhancing recommendation accuracy. Yin et al (2022) proposed a multimodal recommendation model that employed a dual attention mechanism to quantify investor preferences, used deep networks to learn project features, and combined CF mechanisms to model both aspects.…”

Section: Single Domain Book Recommendationmentioning

confidence: 99%

A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning

Cui,

Duan,

Zhang

et al. 2023

International Journal of Data Warehousing and Mining

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Existing book recommendation methods often overlook the rich information contained in the comment text, which can limit their effectiveness. Therefore, a cross-domain recommender system for literary books that leverages multi-head self-attention interaction and knowledge transfer learning is proposed. Firstly, the BERT model is employed to obtain word vectors, and CNN is used to extract user and project features. Then, higher-level features are captured through the fusion of multi-head self-attention and addition pooling. Finally, knowledge transfer learning is introduced to conduct joint modeling between different domains by simultaneously extracting domain-specific features and shared features between domains. On the Amazon dataset, the proposed model achieved MAE and MSE of 0.801 and 1.058 in the “movie-book” recommendation task and 0.787 and 0.805 in the “music-book” recommendation task, respectively. This performance is significantly superior to other advanced recommendation models. Moreover, the proposed model also has good universality on the Chinese dataset.

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Section: Single Domain Book Recommendationmentioning

confidence: 99%

A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning

Cui,

Duan,

Zhang

et al. 2023

International Journal of Data Warehousing and Mining

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“…Experimental results on realistic datasets showed that RICF performs better than traditional item-based collaborative filtering algorithms and state-of-the-art sequence models, such as LSTM and GRU, and is more interpretable. As the scale of the scoring data has increased and auxiliary information is added, researchers have tried to find a way to learn the underlying characteristics by combining neural networks and collaborative filtering [5,6]. Among the current collaborative filtering algorithms, the most effective and common algorithm is to micro-directionally propagate users by extracting potential factors from the user-item scoring matrix based on singular value decomposition (SVD) [7,8].…”

Section: Introductionmentioning

confidence: 99%

Stacked Noise Reduction Auto Encoder–OCEAN: A Novel Personalized Recommendation Model Enhanced

Wang,

Zheng,

Wang

et al. 2024

Systems

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With the continuous development of information technology and the rapid increase in new users of social networking sites, recommendation technology is becoming more and more important. After research, it was found that the behavior of users on social networking sites has a great correlation with their personalities. The five characteristics of the OCEAN personality model can cover all aspects of a user’s personality. In this research, a micro-directional propagation model based on the OCEAN personality model and a Stacked Denoising Auto Encoder (SDAE) was built through the application of deep learning to a collaborative filtering technique. Firstly, the dimension of the user and item feature matrices was lowered using SDAE in order to extract deeper information. The user OCEAN personality model matrix and the reduced user feature matrix were integrated to create a new user feature matrix. Finally, the multiple linear regression approach was used to predict user-unrated goods and generate recommendations. This approach allowed us to leverage the relationships between various factors to deliver personalized recommendations. This experiment evaluated the RMSE and MAE of the model. The evaluation results show that the stacked denoising auto encoder collaborative filtering algorithm can improve the accuracy of recommendations, and the user’s OCEAN personality model improves the accuracy of the model to a certain extent.

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AGRCNet: communicate by attentional graph relations in multi-agent reinforcement learning for traffic signal control

Peng

Rong

et al. 2023

Neural Comput & Applic

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Multimodal deep collaborative filtering recommendation based on dual attention

Cited by 4 publications

References 30 publications

A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning

A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning

Stacked Noise Reduction Auto Encoder–OCEAN: A Novel Personalized Recommendation Model Enhanced

AGRCNet: communicate by attentional graph relations in multi-agent reinforcement learning for traffic signal control

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