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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Vydané v:	Pattern analysis and applications : PAA Ročník 22; číslo 4; s. 1655 - 1666
Hlavní autori:	Zeng, Biqing, Shang, Qi, Han, Xuli, Zeng, Feng, Zhang, Min
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Springer London 01.11.2019 Springer Nature B.V
Predmet:	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
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