基于网络结构与用户内容的动态兴趣识别方法

提出了将社交类服务中的两类极为重要的数据--社交网络结构数据和用户所发布的文本内容数据相结合的动态兴趣识别方法.首先通过定义时间窗口，对社交网络用户的实时文本信息进行主题建模，识别用户实时兴趣概率特征；然后将微观网络结构信息与用户好友的兴趣信息相结合，构建预测特征；最后，建立逻辑回归、支持向量机等分类器，采用所构建的预测特征对用户兴趣进行动态预测.在新浪微博中的应用表明，该方法具备一定的有效性.

Dynamic Interest Identification Based on Social Network Structure and User Generated Contents

Two important data sources in social networks, i. e. the network structure and the user gene-rated contents, were combined to dynamically identify user interest. When building topic models, the topic distributions of contents for each user at each time are obtained. And features used for prediction are extracted by summarizing the topical information based on the social network structure. Finally, these prediction features are exploited to dynamically predict user interest via several classification methods, such as logistic regression and support vector machine. The effectiveness of the proposed method is illustrated based on the Sina Weibo dataset.