CDR-ULPT:Cross-domain recommendation for e-commerce using user-level preferences transfer network-Reference-Cited by-同舟云学术

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CDR-ULPT:Cross-domain recommendation for e-commerce using user-level preferences transfer network

Published:2023-09 Issue: Volume:61 Page:101293
ISSN:1567-4223
Container-title:Electronic Commerce Research and Applications
language:en
Short-container-title:Electronic Commerce Research and Applications

Author:

Zhao Jianli^ORCID,Yao Lutong,Li Tingting,Qu Lijun,Fang Sheng,Zheng Shidong,Gong Maoguo,Li Liang,Li Hao,Zhang Tianheng

Publisher

Elsevier BV

Subject

Management of Technology and Innovation,Marketing,Computer Networks and Communications,Computer Science Applications

Reference46 articles.

1. An improved model of trust-aware recommender systems using reliability measurements;Ahmadian;Inform. Knowled. Technol. (IKT),2014

2. Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach;Ahmadian;Expert Syst. Appl.,2022

3. Cross-domain recommender systems;Cantador,2015

4. DisenCDR: learning disentangled representations for cross-domain recommendation;Cao,2022

5. A multi-view deep learning methods for cross domain user modeling in recommendation systems;Elkahky,2015

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. GTR: An explainable Graph Topic-aware Recommender for scholarly document;Electronic Commerce Research and Applications;2024-09

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