Representation learning: serial-autoencoder for personalized recommendation

doi:10.1007/s11704-023-2441-1

Frontiers of Computer Science

2024, Vol. 18

Issue (4): 184316 https://doi.org/10.1007/s11704-023-2441-1

本期目录

Representation learning: serial-autoencoder for personalized recommendation

Yi ZHU^1,^2,³, Yishuai GENG¹, Yun LI¹(

), Jipeng QIANG¹, Xindong WU^2,³

¹. School of Information Engineering, Yangzhou University, Yangzhou 225127, China
². Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education of the People’s Republic of China), Hefei University of Technology, Hefei 230009, China
³. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China

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Abstract：

Nowadays, the personalized recommendation has become a research hotspot for addressing information overload. Despite this, generating effective recommendations from sparse data remains a challenge. Recently, auxiliary information has been widely used to address data sparsity, but most models using auxiliary information are linear and have limited expressiveness. Due to the advantages of feature extraction and no-label requirements, autoencoder-based methods have become quite popular. However, most existing autoencoder-based methods discard the reconstruction of auxiliary information, which poses huge challenges for better representation learning and model scalability. To address these problems, we propose Serial-Autoencoder for Personalized Recommendation (SAPR), which aims to reduce the loss of critical information and enhance the learning of feature representations. Specifically, we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the input. Second, we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating matrix. The output rating information is used for recommendation prediction. Extensive experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.

Key words： personalized recommendation autoencoder representation learning collaborative filtering

收稿日期: 2022-07-10 出版日期: 2023-06-05

Corresponding Author(s): Yun LI

引用本文:

. [J]. Frontiers of Computer Science, 2024, 18(4): 184316.
Yi ZHU, Yishuai GENG, Yun LI, Jipeng QIANG, Xindong WU. Representation learning: serial-autoencoder for personalized recommendation. Front. Comput. Sci., 2024, 18(4): 184316.

链接本文:

https://academic.hep.com.cn/fcs/CN/10.1007/s11704-023-2441-1
https://academic.hep.com.cn/fcs/CN/Y2024/V18/I4/184316

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Notations	Description
$R$	The rating matrix
$A$	The attributes vectors of all items
$R 1 ′$ , $R 2 ′$	The prediction matrix $R ′ ∈ R n × m$
$n$	The number of users
$m$	The number of items
$r u$	The column of rating matrix
$r i$	The row of rating matrix
$k$	The dimension of item attribute vector
$h$ , $t$	The number of hidden units
$x i$	The $i t h$ instance of original input
$x i ′$	The reconstructed output of $x i$
$s u b (x i ′)$	The part of output of $x i ′$
$ξ$ , $ξ 1$ , $ξ 2$	The hidden feature representation matrix
$W$ , $W 1, 2$ , $W ′$ , $W 1, 2 ′$	The map and remap weight matrix
$b$ , $b ′$ , $b 1 ′$ , $b 2 ′$	The map and remap bias vectors
$f$ , $g$	The nonlinear activation functions
$?$	The product of vectors or matrices

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