YUAN Hanning, CHEN Zhengyu, YANG Jingting, WANG Shuliang, GENG Jing, KE Chuwen. A Hybrid Aspect Based Latent Factor Model for Recommendation[J]. Chinese Journal of Electronics, 2020, 29(3): 482-490. doi: 10.1049/cje.2020.01.004
Citation: YUAN Hanning, CHEN Zhengyu, YANG Jingting, WANG Shuliang, GENG Jing, KE Chuwen. A Hybrid Aspect Based Latent Factor Model for Recommendation[J]. Chinese Journal of Electronics, 2020, 29(3): 482-490. doi: 10.1049/cje.2020.01.004

A Hybrid Aspect Based Latent Factor Model for Recommendation

doi: 10.1049/cje.2020.01.004
Funds:  This work is supported by National Key Research and Development Plan of China (No.2016YFC0803000), Beijing Municipal Science and Technology Project (No.Z171100005117002), and the Open Fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoformation (No.2017NGCMZD03).
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  • Corresponding author: WANG Shuliang (corresponding author) Ph.D., IEEE Senior Member, is a professor in Beijing Institute of Technology in China. His research interests include spatial data mining and software engineering. He was awarded the Fifth Annual InfoSci-Journals Excellence in Research Awards of IGI Global, and one of the best national thesis in China. (Email:slwang2011@bit.edu.cn)
  • Received Date: 2018-12-19
  • Rev Recd Date: 2019-04-04
  • Publish Date: 2020-05-10
  • Recommender system has been recognized as a superior way for solving personal information overload problem. More and more aspect-based models are leveraging user ratings and extracting information from review texts to support recommendation. Aspect-based latent factor model predicts user ratings relying on latent aspect inferred from user reviews. It usually constructs only a single global model for all users, which may be not sufficient to capture the diversity of users’ preferences and leave some items or users be badly modeled. We propose a Hybrid aspect-based latent factor model (HALFM), which jointly optimizes the Global aspect-based latent factor model (GALFM) and the Local Aspect-based Latent Factor Models (LALFM), their user-specific combination, and the assignment of users to the LALFMs. HALFM makes prediction by combining user-specific of GALFM and many LALFMs. Experimental results demonstrate that the proposed HALFM outperforms most of aspectbased recommendation techniques in rating prediction.
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