A mixed heterogeneous factorization model for non-overlapping cross-domain recommendation

Yu, Ting; Guo, Jintang; Li, Wenhua; Lü, Meng

doi:10.1016/j.dss.2021.113625

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…At the same time, useful information was selected and shared through a denoising autoencoder (DAE). Yu, T., et al (2021) proposed a hybrid heterogeneous decomposition model for extracting and transferring shared knowledge between the auxiliary and target domains. The model captures shared knowledge and domain features based on adaptive tensor decomposition and biased matrix decomposition, respectively, and then accumulates and combines them to obtain recommendation results.…”

Section: Cdrmentioning

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: Cdrmentioning

confidence: 99%