title: Local regression transfer learning with applications to users' psychological characteristics prediction.
abstract: It is important to acquire web users' psychological characteristics. Recent studies have built computational models for predicting psychological characteristics by supervised learning. However, the generalization of built models might be limited due to the differences in distribution between the training and test dataset. To address this problem, we propose some local regression transfer learning methods. Specifically, k-nearest-neighbour and clustering reweighting methods are developed to estimate the importance of each training instance, and a weighted risk regression model is built for prediction. Adaptive parameter-setting method is also proposed to deal with the situation that the test dataset has no labels. We performed experiments on prediction of users' personality and depression based on users of different genders or different districts, and the results demonstrated that the methods could improve the generalization capability of learning models.
authors.affiliation: Business School, Shandong Jianzhu University, Jinan, China.
authors.forename: Zengda
authors.initials: Z
authors.lastname: Guan
authors.affiliation: Department of Psychology, Beijing Forestry University, Beijing, China.|Black Dog Institute, University of New South Wales, Sydney, Australia.
authors.forename: Ang
authors.initials: A
authors.lastname: Li
authors.affiliation: Institute of Psychology, Chinese Academy of Sciences, Beijing, China. tszhu@psych.ac.cn.|Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China. tszhu@psych.ac.cn.
authors.forename: Tingshao
authors.initials: T
authors.lastname: Zhu
keywords: Covariate shift; Local transfer learning; Psychological characteristics prediction