关键词: 方面分预测, 异质信息网络, 主题模型, 结构信息, 推荐系统

Abstract: Concern the problem that traditional aspect rating prediction methods just pay attention to textual information while ignoring the structural information in the review network, a novel Aspect rating prediction method based on Heterogeneous Information Network and topic modeling (HINToAsp) was proposed for effectively integering textual information and structural information. Firstly, a new review topic model of opinion phrases called Phrase-PLSA was put forward to integrate textual information of reviews and ratings for mining aspect topics. And then, considering the rich structural information among users, reviews, and items, a topic propagation model was designed by the aid of constructing “U-R-S” heterogeneous information network. Finally, a random walk framework was used to combine textual information and structural information effectively, which insured an accurate aspect rating prediction. Experimental results on both Dianping corpora and TripAdvisor corpora demonstrate that HINToAsp is more effective than recent methods like the Quad-tuples PLSA (QPLSA) model, the Gaussian distribution for RAting Over Sentiments (GRAOS) model and the Sentiment-Aligned Topic Model (SATM), and indicate the better performance on recommendation system.

Key words: aspect rating prediction, heterogeneous information network, topic modeling, structural information, recommendation system