An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

YAN Wenjie; ZHANG Jiahao; LI Ziqi

doi:10.23919/cje.2022.00.034

Volume 32 Issue 1

Jan. 2023

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YAN Wenjie, ZHANG Jiahao, LI Ziqi, “An Interactive Perception Method Based Collaborative Rating Prediction Algorithm,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 97-110, 2023, doi: 10.23919/cje.2022.00.034

Citation:

YAN Wenjie, ZHANG Jiahao, LI Ziqi, “An Interactive Perception Method Based Collaborative Rating Prediction Algorithm,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 97-110, 2023, doi: 10.23919/cje.2022.00.034

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An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

doi: 10.23919/cje.2022.00.034

1.
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China

Funds: This work was supported by the National Natural Science Foundation of China (61702157)

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Author Bio:
Wenjie YAN was born in Hebei Province. He received the Ph.D. degree from the Harbin Institute of Technology. He is currently an Associate Professor with the School of Artificial Intelligence, Hebei University of Technology. His current research interests include deep learning and data mining. (Email: wenjieyanhit@163.com)

Jiahao ZHANG was born in Shandong Province. He received the M.S. degree with the School of Artificial Intelligence, Hebei University of Technology. His current research interests include deep learning and data mining

Ziqi LI was born in Tianjin, China. She is currently pursuing her master degree at the School of Artificial Intelligence, Hebei University of Technology. Her current research interests include deep learning and data mining
Received Date: 2022-03-05
Accepted Date: 2022-06-27

Available Online: 2022-07-16

Publish Date: 2023-01-05

Abstract

Abstract

To solve the rating prediction problems of low accuracy and data sparsity on different datasets, we propose an interactive perception method based collaborative rating prediction algorithm named DCAE-MF, by fusing dual convolutional autoencoder (DCAE) and probability matrix factorization (PMF). Deep latent representations of users and items are captured simultaneously by DCAE and are deeply integrated with PMF to collaboratively make rating predictions based on the known rating history of users. A global multi-angle collaborative optimization learning method is developed to effectively optimize all the parameters of DCAE-MF. Extensive experiments are performed on seven real-world datasets to demonstrate the superiority of DCAE-MF on key rating accuracy metrics of the root mean squared error (RMSE) and mean absolute error (MAE).
- Recommender systems,
- Probabilistic matrix factorization,
- Convolutional autoencoder,
- Interactive perception,
- Multi-angle optimization

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References(31)

References

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