基于时间的关联模型

Mostafa Keikha, Shima Gerani, F. Crestani
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引用次数: 39

摘要

本文讨论博客提要检索,其目标是为给定的用户查询检索最相关的博客提要。由于检索单元是一个博客,作为文章的集合,执行相关反馈技术和选择最合适的文档进行查询扩展变得具有挑战性。假设时间是博客文章内容的有效参数,提出了一种基于时间的查询扩展方法。在这种方法中,我们使用与查询最相关的日期(而不是最相关的文档)来选择要展开的术语。这为我们的扩张提供了更可靠的条件。我们在Blog08上的初步实验表明,该方法在博客检索方面优于当前相关反馈方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time-based relevance models
This paper addresses blog feed retrieval where the goal is to retrieve the most relevant blog feeds for a given user query. Since the retrieval unit is a blog, as a collection of posts, performing relevance feedback techniques and selecting the most appropriate documents for query expansion becomes challenging. By assuming time as an effective parameter on the blog posts content, we propose a time-based query expansion method. In this method, we select terms for expansion using most relevant days for the query, as opposed to most relevant documents. This provide us with more trustable terms for expansion. Our preliminary experiments on Blog08 collection shows that this method can outperform state of the art relevance feedback methods in blog retrieval.
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