Promoting Document Relevance Using Query Term Proximity for Exploratory Search

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS International Journal of Information Retrieval Research Pub Date : 2023-06-27 DOI:10.4018/ijirr.325072
Vikram Singh
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Abstract

In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e., term frequency (tf), inverse document frequency (idf), etc. Query term proximity (QTP) within matched documents is mostly under-explored. In this article, a novel information retrieval framework is proposed to promote the documents among all relevant retrieved ones. The relevance estimation is a weighted combination of document statistics and query term statistics, and term-term proximity is simply aggregates of diverse user preferences aspects in query formation, thus adapted into the framework with conventional relevance measures. Intuitively, QTP is exploited to promote the documents for balanced exploitation-exploration, and eventually navigate a search towards goals. The evaluation asserts the usability of QTP measures to balance several seeking tradeoffs, e.g., relevance, novelty, result diversification (coverage, topicality), and overall retrieval. The assessment of user search trails indicates significant growth in a learning outcome (due to novelty).
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利用探索性搜索中的查询词邻近度提高文档相关性
在信息检索系统中,相关性的表现是至关重要的,并且是有规律地基于文献术语统计的,即词频(term frequency, tf)、逆文献频率(inverse document frequency, idf)等。匹配文档中的查询词接近性(QTP)还没有得到充分的研究。本文提出了一种新的信息检索框架,用于在所有相关的检索文档中促进文档的检索。相关性估计是文档统计和查询词统计的加权组合,术语接近度是查询信息中不同用户偏好方面的简单聚合,因此适用于具有常规相关性度量的框架。直观地说,QTP被用来促进文档的平衡利用和探索,并最终引导搜索实现目标。评估断言QTP度量的可用性,以平衡几个寻求权衡,例如,相关性、新颖性、结果多样化(覆盖范围、话题性)和整体检索。对用户搜索轨迹的评估表明学习结果的显著增长(由于新颖性)。
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来源期刊
International Journal of Information Retrieval Research
International Journal of Information Retrieval Research COMPUTER SCIENCE, INFORMATION SYSTEMS-
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64
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