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基于自适应隐反馈用户行为模型的数据源选择 被引量:1

Database selection based on adaptively implicit feedback user action model
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摘要 当前搜索引擎用户个性化的研究是搜索引擎优化的一个研究分支。当前检索模型的主要弊端就是搜索引擎用户提供的信息很少。目前主要借助于用户在和元搜索引擎交互的过程中提供的隐反馈信息对成员搜索引擎的数据源选择算法进行优化,利用语言模型对用户检索行为建模,用户与元搜索引擎交互的过程中动态更新用户行为模型,自适应的满足不同检索动机的用户的信息需求。 Currently search personalization is a hot research point on the branch of search engine optimization. A major limitation of most existing retrieval model is that user offer less information about what he need. How to exploit implicit feedback information when user interacts with meta-search engine to improve database selection is researched. User action information is constructed by using language model, and different user information retrieval need is adaptively satisfied by updating user action model actively when user interact with meta-search engine.
作者 李鹏 阳小华
出处 《计算机工程与设计》 CSCD 北大核心 2007年第12期2949-2950,2970,共3页 Computer Engineering and Design
基金 湖南省科技厅2006年计划基金项目(2006GK3086)
关键词 语言模型 元搜索 隐式相关反馈 数据源选择 用户个性化 language model meta-search implicit feedback database selection user personalization
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