中文摘要：

学术搜索引擎是一种行业化的搜索引擎,但因其缺乏个性化的服务,使得用户的学术文献检索效率低下,海量的数字学术资源得不到充分利用。本文使用Google翻译,研究基于机器翻译的中、英、俄、法和西班牙等五个语种跨语言学术检索。在跨语言学术搜索的基础上研究个性化检索技术,提出一种基于聚类的个性化信息检索方法：通过观察用户对搜索结果聚类的点击行为,生成并更新用户实时兴趣模型,采用余弦夹角公式计算用户实时兴趣模型与搜索返回结果的相似度,根据相似度大小,为用户提供个性化重排序的搜索返回结果。实验结果证明了提出方法的有效性。

英文摘要：

The academic search engine is a domain-oriented search engine.However,due to its lack of personalized services,there appeared the problem of inefficiency in literature retrieval and insufficient usage of massive digital academic resource.This paper employs Google translation,presents a Chinese,English,Russia,French and Spanish cross-language academic search engine based on machine translation.On the foundation of cross-language academic search,we research on personalized information retrieval techniques,propose a personalized information retrieval approach based on clustering： based on the click behavior of the clusters achieved by search results clustering,generates and updates user real-time profile,employs cosine formula compute the similarities between the user real-time profile and search results,finally personalized resorts the search results based on the similarities.The experimental results show that the proposed approach has its effectiveness and users acceptance.