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Local Regression Transfer Learning for Users Personality Prediction
Guan, ZD (Guan, Zengda); Guan, ZD (Guan, Zengda); Nie, D (Nie, Dong); Hao, BB (Hao, Bibo); Bai, ST (Bai, Shuotian); Zhu, TS (Zhu, Tingshao)
第一作者Guan, ZD (Guan, Zengda)
2014
会议名称10th International Conference on Active Media Technology (AMT) held as part of the Web Intelligence Congress (WIC)
通讯作者邮箱tszhu@psych.ac.cn
会议录名称ACTIVE MEDIA TECHNOLOGY, AMT 2014
卷号8610
期号不详
页码23-34
会议日期AUG 11-14, 2014
会议地点Univ Warsaw, Warsaw, POLAND
会议举办国POLAND
摘要

    Some research has been done to predict users' personality based on their web behaviors. They usually use supervised learning methods to model on training dataset and predict on test dataset. However, when training dataset has different distributions from test dataset, which doesn't meet independently identical distribution condition, traditional supervised learning models may perform not well on test dataset. Thus, we introduce a new regression transfer learning framework to deal with this problem, and propose two local regression instance-transfer methods. We use clustering and k-nearest-neighbor to reweight importance of each training instance to adapt to test dataset distribution, and then train a weighted risk regression model for prediction. We perform experiments on the condition that users dataset are from different genders and from different districts, and the results indicate that our methods can reduce mean square error about 30% to the most compared with non-transfer methods and be better than other transfer method in the whole.

关键词Local Regression Transfer Learning Importance Reweighting Personality Prediction
ISBN号978-3-319-09912-5; 978-3-319-09911-8
语种英语
WOS记录号WOS:000349148900003
引用统计
文献类型会议论文
条目标识符http://ir.psych.ac.cn/handle/311026/26349
专题社会与工程心理学研究室
作者单位Chinese Acad Sci, Inst Psychol, Beijing
推荐引用方式
GB/T 7714
Guan, ZD ,Guan, ZD ,Nie, D ,et al. Local Regression Transfer Learning for Users Personality Prediction[C],2014:23-34.
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