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.

中文翻译：

---

局部回归转移学习及其在用户心理特征预测中的应用。

获取网络用户的心理特征很重要。最近的研究已经建立了通过监督学习预测心理特征的计算模型。但是，由于训练和测试数据集之间的分布差异，构建模型的泛化可能会受到限制。为了解决这个问题，我们提出了一些局部回归转移学习方法。具体来说，开发了k最近邻和聚类重加权方法来估计每个训练实例的重要性，并建立了加权风险回归模型进行预测。还提出了自适应参数设置方法，以应对测试数据集没有标签的情况。我们进行了有关用户预测的实验