显示用户知识的网页搜索片段分析

Jumpei Yamada, D. Kitayama
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引用次数: 0

摘要

近年来,由于互联网的广泛使用,使用搜索引擎搜索网络的机会不断增加。在传统的搜索引擎中,信息检索是通过反复输入查询并在搜索引擎结果页面(serp)中选择和浏览每个页面来实现的。搜索引擎提供标题、摘要和其他信息,以帮助用户选择合适的Web页面。然而,由于缺乏先验知识或搜索策略失败,人们会逐个查看Web页面。为了解决这个问题,我们在serp中提供了未访问结果中的关键字,以便用户可以预测网页的内容。我们提出了两种特征词作为扩展片段呈现在每个搜索结果中:内容词表示网页的中心内容,已知主题词和未知主题词表示通过浏览网页可以获得的知识程度。它们的提取是基于片段句子中的词的聚类,分别使用词的分布式表示和访问页面中的词的聚类。我们调查了提议的扩展片段对用户搜索行为的影响。实验结果表明,我们的方法在某些类型的搜索中是有用的,因为它减少了完成搜索所需的时间。此外,参与者对扩展片段的评分是有利的,特别是那些未知主题词。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Analysis of Web Search Snippets Displaying User's Knowledge
In recent years, due to the widespread use of the Internet, the number of opportunities to search the Web using search engines has been increasing. In conventional search engines, information retrieval is achieved by repeatedly entering a query and selecting and browsing each page in the search engine result pages (SERPs). The search engines present titles, snippets, and other information to help users select suitable Web pages. However, there are cases in which people view Web pages one by one due to lack of prior knowledge or failure of search strategies. To solve this problem, we present keywords from unvisited results in the SERPs, so that users can predict the content of the Web pages. We propose two kinds of feature words as extended snippets to be presented in each search result: a content word to indicate the central content of a Web page and known-topic and unknown-topic words to indicate the degree of knowledge that one would gain by browsing the Web page. The extraction of those is based on the clustering of words in snippet sentences using the distributed representation of the words and the clustering of words in the visited pages, respectively. We investigated the impact of the proposed extended snippet on user search behavior. The experimental findings indicate that our method was useful in certain types of search, as it decreased the time necessary to complete the search. Furthermore, the participants' ratings of the extended snippets were favorable, especially those of the unknown-topic words.
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