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2010 – 2019
- 2017
[j2]Jing Wang
, Lanfen Lin, Heng Zhang, Jiaqi Tu, Penghua Yu:
A novel confidence estimation method for heterogeneous implicit feedback. Frontiers Inf. Technol. Electron. Eng. 18(11): 1817-1827 (2017)- [j1]Penghua Yu, Lanfen Lin, Jing Wang:
A novel framework to alleviate the sparsity problem in context-aware recommender systems. New Rev. Hypermedia Multim. 23(2): 141-158 (2017) - 2016
- [c8]Penghua Yu, Lanfen Lin:
SeBPR: Semantics Enhanced Bayesian Personalized Ranking with Comparable Item Pairs. ICDM Workshops 2016: 1015-1022 - [c7]Penghua Yu, Lanfen Lin, Yuangang Yao:
A Novel Framework to Process the Quantity and Quality of User Behavior Data in Recommender Systems. WAIM (1) 2016: 231-243 - 2015
- [c6]Penghua Yu, Lanfen Lin, Jing Wang:
User Preference Quantity versus Recommendation Performance: A Preliminary Study. SmartCity 2015: 844-849 - [c5]Penghua Yu, Lanfen Lin, Zeyang Li:
Human Factors Based Partitioning Versus Data Clustering for Recommendations. WI-IAT (1) 2015: 276-279 - 2014
- [c4]Penghua Yu, Lanfen Lin, Ruisong Wang, Jing Wang, Feng Wang:
A unified latent factor correction scheme for collaborative filtering. FSKD 2014: 581-586 - [c3]Penghua Yu, Lanfen Lin, Feng Wang, Jing Wang, Meng Wang:
Improving Recommendations with Collaborative Factors. WAIM 2014: 30-33 - 2013
- [c2]Jing Wang, Lanfen Lin, Feng Wang, Penghua Yu, Jiaolong Liu, Xiaowei Zhu:
Extracting Novel Features for E-Commerce Page Quality Classification. ADMA (1) 2013: 481-492 - [c1]Meng Wang, Lanfen Lin, Jing Wang, Penghua Yu, Jiaolong Liu, Fei Xie:
Improving Short Text Classification Using Public Search Engines. IUKM 2013: 157-166
Coauthor Index
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