Open AccessA Hybrid Aspect Based Latent Factor Model for Recommendation
Author(s) -
Yuan Hanning,
Chen Zhengyu,
Yang Jingting,
Wang Shuliang,
Geng Jing,
Ke Chuwen
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.01.004
Subject(s) - computer science , factor (programming language) , recommender system , factor analysis , information overload , machine learning , artificial intelligence , data mining , world wide web , programming language
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.