Fusion topic model transfer learning for cross-domain recommendation

2014 ◽  
Author(s):  
Lei Wu ◽  
Wensheng Zhang ◽  
Jue Wang
Keyword(s):  
Transfer Learning ◽  
Topic Model ◽  
Cross Domain ◽  
Model Transfer

scholarly journals Cross-Domain Latent Modulation for Variational Transfer Learning

2021 ◽  
Author(s):  
Jinyong Hou ◽  
Jeremiah D. Deng ◽  
Stephen Cranefield ◽  
Xuejie Ding
Keyword(s):  
Transfer Learning ◽  
Cross Domain

Deep transfer learning mechanism for fine-grained cross-domain sentiment classification

2021 ◽  
pp. 1-18
Author(s):  
Zixuan Cao ◽  
Yongmei Zhou ◽  
Aimin Yang ◽  
Sancheng Peng
Keyword(s):  
Transfer Learning ◽  
Sentiment Classification ◽  
Learning Mechanism ◽  
Fine Grained ◽  
Cross Domain

scholarly journals Cross-domain citation recommendation based on hybrid topic model and co-citation selection

2017 ◽  
Vol 9 (3) ◽  
pp. 220
Author(s):  
Supaporn Tantanasiriwong ◽  
Sumanta Guha ◽  
Paul Janecek ◽  
Choochart Haruechaiyasak ◽  
Leif Azzopardi
Keyword(s):  
Topic Model ◽  
Cross Domain

Joint adaptive transfer learning network for cross-domain fault diagnosis based on multi-layer feature fusion

2021 ◽  
Author(s):  
Yimin Jiang ◽  
Tangbin Xia ◽  
Dong Wang ◽  
Kaigan Zhang ◽  
Lifeng Xi
Keyword(s):  
Fault Diagnosis ◽  
Transfer Learning ◽  
Feature Fusion ◽  
Learning Network ◽  
Cross Domain

Enhanced cross-domain sentiment classification utilizing a multi-source transfer learning approach

2018 ◽  
Vol 23 (14) ◽  
pp. 5431-5442 ◽  
Author(s):  
Farhan Hassan Khan ◽  
Usman Qamar ◽  
Saba Bashir
Keyword(s):  
Transfer Learning ◽  
Sentiment Classification ◽  
Learning Approach ◽  
Cross Domain

Tri-training for Dependency Parsing Domain Adaptation

2022 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Shu Jiang ◽  
Zuchao Li ◽  
Hai Zhao ◽  
Bao-Liang Lu ◽  
Rui Wang
Keyword(s):  
Transfer Learning ◽  
High Performance ◽  
Domain Adaptation ◽  
Language Model ◽  
Training Methods ◽  
Dependency Parsing ◽  
Cross Domain ◽  
Cross Lingual ◽  
Domain Transfer ◽  
Domain Transfer Learning

In recent years, the research on dependency parsing focuses on improving the accuracy of the domain-specific (in-domain) test datasets and has made remarkable progress. However, there are innumerable scenarios in the real world that are not covered by the dataset, namely, the out-of-domain dataset. As a result, parsers that perform well on the in-domain data usually suffer from significant performance degradation on the out-of-domain data. Therefore, to adapt the existing in-domain parsers with high performance to a new domain scenario, cross-domain transfer learning methods are essential to solve the domain problem in parsing. This paper examines two scenarios for cross-domain transfer learning: semi-supervised and unsupervised cross-domain transfer learning. Specifically, we adopt a pre-trained language model BERT for training on the source domain (in-domain) data at the subword level and introduce self-training methods varied from tri-training for these two scenarios. The evaluation results on the NLPCC-2019 shared task and universal dependency parsing task indicate the effectiveness of the adopted approaches on cross-domain transfer learning and show the potential of self-learning to cross-lingual transfer learning.

Group-Based Deep Transfer Learning with Mixed Gate Control for Cross- Domain Recommendation

2021 ◽  
Author(s):  
Mingze Sun ◽  
Daiyue Xue ◽  
Weipeng Wang ◽  
Qifu Hu ◽  
Jianping Yu
Keyword(s):  
Transfer Learning ◽  
Cross Domain ◽  
Gate Control
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