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Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust

机译:模糊集与信任度混合的智能信息检索

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The main challenge for effective web Information Retrieval(IR) is to infer the information need from user’s query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user’s information need. Trust based web page recommendations retrieve search results according to the user’s information need. In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user’s information need and trust is used for recommendation of web pages according to the user’s information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.
机译:有效的网络信息检索(IR)的主要挑战是从用户的查询中推断出信息需求并检索相关文档。由于用户查询的含糊和不精确,搜索结果的准确性较低,因此无法检索到足够的相关文档。基于模糊集的查询扩展可处理不精确和模糊的查询,以推断用户的信息需求。基于信任的网页建议根据用户的信息需求检索搜索结果。本文设计了一种在网络查询会话挖掘中使用模糊集和信任度混合的智能信息检索算法,以执行模糊查询扩展来推断用户的信息需求,并根据用户的信息需求将信任度用于推荐网页。对学术,娱乐和体育领域收集的数据集进行了实验,搜索结果证实了准确性的提高。

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