Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (2): 59-77.

doi: 10.11871/jfdc.issn.2096-742X.2020.02.005

Special Issue: “数据分析技术与应用”专刊

• Special Issue: Data Analysis Technology & Application • Previous Articles     Next Articles

Modeling the Effects of Individual and Group Heterogeneity on Multi-Aspect Rating Behavior

Liu Kunpeng1,Zhao Xiaosa2,Hu Yirui3,Fu Yanjie1,*()   

  1. 1. Department of Computer Science, University of Central Florida, Orlando, FL 32816, United States
    2. School of Information Science and Technology, Northeast Normal University, Changchun, Jilin 130024, China
    3. Population Health Sciences, Geisinger, Danville, PA 17822, United States
  • Received:2019-12-10 Online:2020-04-20 Published:2020-06-03
  • Contact: Yanjie Fu


[Objective] Multi-aspect rating system could help customers better understand the item or service, because it provides not only the overall rating but also more detailed aspect ratings. By modeling the rating patterns on multi-aspect rating systems, we can better find out latent rating groups and quantitatively understand the rating behaviors lie in these groups. This can also help service providers improve their service and attract more targeted customers. However, due to the complex nature of multi-aspect rating system, it is challenging to model its rating patterns. [Methods] To address this problem, in this paper, we propose a two-step framework to learn the rating patterns from multi-aspect rating systems. Specifically, we first propose a multi-factorization relationship learning (MFRL) method to obtain the user and item aspect factor matrices. In MFRL, we unify matrix factorization, multi-task learning and task relationship learning into one optimization framework. And then, we model the rating patterns by exploiting group-wise overall rating prediction via mixture regression, whose inputs are the factor vectors of users and items learned from MFRL method. [Results] We apply the proposed framework on a real-world dataset (i.e., the crawled hotel rating dataset from to evaluate the performance of our proposed method. Extensive experimental results demonstrate the effectiveness of the proposed framework. [Conclusions] Individual and Group Heterogeneity could affect the behaviors behind the rating acts, which should be taken into account in modeling the rating patterns.

Key words: Multi-Aspect Rating, Recommender System, Multi-Task Learning, Relationship Learning, User Behavior