基于兴趣偏好的微博用户性别推断研究

用户属性，如：性别、年龄等，是计算心理学、个性化搜索、社会化商业推广等研究和应用考察的核心因素。利用用户生成数据自动推断用户属性成为新兴的研究课题。本文提出基于用户兴趣偏好研究微博用户的性别推断问题。考察了用户内容偏好以及关注行为偏好对性别推断的作用。在新浪微博近万名用户的数据集上证明了用户偏好特征的有效性。与传统的语用特征相比，将用户内容偏好与关注偏好相结合能够显著提高推断准确率。关注偏好特征对推断非活跃用户的性别尤其有效。

User I nterest Preferences for Gender I nference on MicrobIog

User demographic attributes,such as gender and age,are the core factors to be considered for research and applications in computational psychology,personalized search and social commerce marketing.Automatic user latent attribute inference based on user generated data becomes an emerging research topic.This paper proposes a methed for user gender in-ference on Microblog by exploiting user content preferences and following behaviour preferences.The experiments on a data-set collected from Sina Weibo that consists of nearly 10000 users demonstrate the effectiveness of user preferences features. Comparing with the traditional language usage features,combining user content preferences and user following preferences features can improve the inference accuracy largely.The user following preferences features are especially effective for infer-ring the gender of inactive users.