数据与计算发展前沿 ›› 2020, Vol. 2 ›› Issue (2): 59-77.doi: 10.11871/jfdc.issn.2096-742X.2020.02.005

• 专刊: 数据分析技术与应用 • 上一篇    下一篇

个体及团体异构多方面评分行为建模

刘鲲鹏1,赵宵飒2,胡一睿3,傅衍杰1,*()   

  1. 1. 中佛罗里达大学,计算机系,美国 佛罗里达 奥兰多,32816
    2. 东北师范大学,信息科学与技术学院,中国 吉林 长春,130024
    3. 盖辛格,人口健康科学,美国 宾夕法尼亚 丹维尔,17822
  • 收稿日期:2019-12-10 出版日期:2020-04-20 发布日期:2020-06-03
  • 通讯作者: 傅衍杰 E-mail:yanjie.fu@ucf.edu
  • 作者简介:Liu Kunpeng is a Ph.D. student in University of Central Florida. His research interests are data mining and automated data science.
    Role in this paper: Responsible for the formula derivation, experiment design and paper writing.
    E-mail: kunpengliu@knights.ucf.edu|Zhao Xiaosa is a Ph.D. student in Northeast Normal University. Her research interests are data mining and bioinformatics.
    Role in this paper: Responsible for literature review and paper formatting.
    E-mail: zhaoxs686@nenu.edu.cn|Hu Yirui got her Ph.D. degree from Department of Statistics, Rutgers, the State University of New Jersey in 2016. She is an Assistant Professor in Geisinger. Her research interests are statistics, biostatistics and predictive modeling.
    Role in this paper: Responsible for coordinating the paper writing.
    E-mail: yhu1@geisinger.edu|Fu Yanjie got his Ph.D. degree from Department of Management Science and Information Systems, Rutgers, the State University of New Jersey in 2016. He is an Assistant Professor in University of Central Florida. His research interest are data mining, spatial and mobile computing, and automated data science.
    Role in this paper: Contact Author. Responsible for framework design and attending technical discussion on technologies.

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 E-mail:yanjie.fu@ucf.edu

摘要:

【目的】除了提供总体评分,多方面评分系统还可以提供更详细的方面评分,因此它可以帮助消费者更好地理解商品和服务。通过对多方面评分系统评分模式的建模,我们可以更好地发现潜在的评分组以及定量地理解这些评分组的评分行为。另外,这种建模也可以帮助服务提供者更好地改进他们的服务以吸引更多消费者。但是,由于多方面评分系统的复杂特性,对它的建模存在很多挑战。【方法】为了解决这些问题,本文提出了一种两步框架来从多方面评分系统中学习评分模式。详细地说,我们首先提出一种多分解关系学习方法(MFRL)来得到用户和商品的方面因素矩阵。在MFRL中,我们将矩阵分解,多任务学习和任务关系学习引入到同一个优化框架内。然后,我们将MFRL学习得来的用户和商品向量表征作为输入,通过高斯混合模型来构建组与组之间总体评分预测。【结果】我们在真实数据集上验证了提出的研究框架。大量实验结果表明我们提出的方法的有效性。【结论】用户异质性会潜在地影响用户的评分行为,因此在对个体及团体的评分行为进行建模时,要充分考虑到目标异质性带来的影响。

关键词: 多方面评分, 推荐系统, 多任务学习, 关系学习, 用户行为

Abstract:

[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 TripAdvisor.com) 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