实验室关于个性化检索研究相关成果被期刊Soft Computing录用
新闻来源:IR实验室       发布时间:2019/4/16 18:43:39

  近日, 收到期刊《Soft Computing》编辑部邮件,实验室徐博等的研究工作“Integrating Social Annotations into Topic Models for Personalized Document Retrieval”已被录用。

  摘要:社会化标注是一种重要的网络资源,涵盖丰富的用户偏好信息,基于社会化标注的信息检索研究一直以来是个性化检索领域重要研究内容。由于社会化标注资源同时涉及用户、文档和标签等信息,如何综合利用这些信息有待深入研究。本文提出基于社会化标注改进文档主题模型,首先基于文档标签面向用户查询意图重构候选文档,让文档聚焦于用户偏好;然后基于用户、文档和标签三者的关系优化潜在狄利克雷主题分布,以获得个性化文档主题分布;最后将个性化文档主题分布融入查询似然语言模型,以满足用户的个性化信息需求。实验结果验证了本文方法的有效性。

  AbstractSocial annotations are valuable resources generated by users on the Web, which encode abundant information on user preferences for certain documents. Social annotation based information retrieval has been studied in recent years for personalizing search results and fulfilling user information needs. However, since social annotations are complicated and associated with users, documents and tags simultaneously, it remains a great challenge to fully capture the potentially useful information for improving retrieval performance. To meet the challenge, we propose a novel method to integrate social annotations into topic models for personalized document retrieval. Our method first reconstructs candidate documents for a given query using social tags of documents to capture user preferences. The reconstructed documents are tailored to user preferences for achieving better performance. We then generalize the latent Dirichlet allocation-based topic models by considering the relationship among users, social tags and documents from social annotations. The modified topic model optimizes the distribution of latent topics of documents for different users to meet user information needs. Experimental results show that our method can significantly outperform the state-of-the-art baseline models for improving the performance of personalized retrieval.