RACMF: robust attention convolutional matrix factorization for rating prediction

Matrix factorization is widely used in collaborative filtering, especially when the data are extremely large and sparse. To deal with the scale and sparsity problem of data, several recommender models adopt users and items’ side information to improve the recommendation results. However, some existi...

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Veröffentlicht in:	Pattern analysis and applications : PAA Jg. 22; H. 4; S. 1655 - 1666
Hauptverfasser:	Zeng, Biqing, Shang, Qi, Han, Xuli, Zeng, Feng, Zhang, Min
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.11.2019 Springer Nature B.V
Schlagworte:	Artificial neural networks Computer Science Datasets Factorization Industrial and Commercial Application Model accuracy Noise reduction Pattern Recognition Sparsity Attention mechanism Additional stacked denoising autoencoder Convolutional neural network Rating prediction
ISSN:	1433-7541, 1433-755X
Online-Zugang:	Volltext
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