个性化信息检索方法研究

个性化信息检索是十分有用的检索方法,用户模型能够表示用户个人的爱好与兴趣,有许多研究工作以各种方式使用用户模型扩充问句.提出一种新的基于一元语言模型的方法.它通过对包含多个主题域的长期用户模型的学习得到相关的语义内容,对问句进行扩展后进行检索,得到更接近用户兴趣的结果,然后再与伪相关反馈模型相结合,进一步提高检索性能.通过实验证明,该方法取得了较好的效果.

Research of Personalized Methods of Information Retrieval

Personalization to information retrieval is very useful in information retrieval, the user profile can be used to represent the favorites or interests of an user. Many approaches to personali}ation have been studied in expanding query with user profile. We proposed a navel method which use the context of long-term user profile with multiple-domain to expand query model under the unigram language modeling framework, uses the new query model to retrieve and get more interesting results for users. Then combined with psudo-elevance feedback model, the method get better performance. Experimental results show that the proposed method is effective.