Context Matcher: Improved Web Search Using Query Term Context in Source Document and in Search Results

Author(s):  
Takahiro Kawashige ◽  
Satoshi Oyama ◽  
Hiroaki Ohshima ◽  
Katsumi Tanaka
Author(s):  
David N. Aurelio ◽  
Ronald R. Mourant

Users of Web search engines report two main problems: an insufficient number of relevant results and the mixing of relevant results with irrelevant results. Therefore, this research investigates the effects of query ambiguity and three forms of sorting search results on user performance and preference. Forty-eight Web search engine users evaluated three forms of sorting results. For each task, the query was a single term that had one, two, or three meanings. The results indicated that the preferred results sorting method was affected by the page number of the correct results. When the correct results were all located on the same page (i.e., the first page), the participants preferred the Ranking, Disambiguating, and Categories methods when the query term had one, two and three meanings, respectively. When the correct results were not on the first page, the test participants preferred the Categories sorting method for all number of query meanings.


2021 ◽  
pp. 089443932110068
Author(s):  
Aleksandra Urman ◽  
Mykola Makhortykh ◽  
Roberto Ulloa

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.


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