Using a Fuzzy Model for Combining Search Results from Different Information Sources to Build a Metasearch Engine

2006 ◽  
pp. 325-334 ◽  
Author(s):  
Wiratna S. Wiguna ◽  
Juan J. Fernández-iébar ◽  
Ana García-Serrano
Keyword(s):  
Information Sources ◽  
Fuzzy Model ◽  
Search Results ◽  
Metasearch Engine

scholarly journals Tool to Retrieve Less-Filtered Information from the Internet

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 65
Author(s):  
Yuta Nemoto ◽  
Vitaly Klyuev
Keyword(s):  
Social Networking ◽  
Communication Technology ◽  
Search Engines ◽  
General Purpose ◽  
Main Role ◽  
Practical Application ◽  
Social Networking Services ◽  
Search Results ◽  
Metasearch Engine ◽  
Filter Bubble

While users benefit greatly from the latest communication technology, with popular platforms such as social networking services including Facebook or search engines such as Google, scientists warn of the effects of a filter bubble at this time. A solution to escape from filtered information is urgently needed. We implement an approach based on the mechanism of a metasearch engine to present less-filtered information to users. We develop a practical application named MosaicSearch to select search results from diversified categories of sources collected from multiple search engines. To determine the power of MosaicSearch, we conduct an evaluation to assess retrieval quality. According to the results, MosaicSearch is more intelligent compared to other general-purpose search engines: it generates a smaller number of links while providing users with almost the same amount of objective information. Our approach contributes to transparent information retrieval. This application helps users play a main role in choosing the information they consume.

Comparison of the different electronic versions of the Encyclopaedia Britannica: a usability study

2003 ◽  
Vol 21 (6) ◽  
pp. 546-554
Author(s):  
B. Hamilton
Keyword(s):  
Web Sites ◽  
Information Sources ◽  
Online Version ◽  
Usability Study ◽  
Cd Rom ◽  
Search Results ◽  
Set Up ◽  
Magazine Articles ◽  
Initial Results

Over the years, Encyclopaedia Britannica has undergone a number of revisions and has been provided in a number of formats other than print. The different electronic formats include a CD‐ROM version, a subscription online version, a free online version that was recently changed to a subscription version, and a DVD‐ROM version. The purpose of this study was to evaluate the usability and effectiveness of the different electronic versions of the Encyclopaedia Britannica. The objectives of this study were to find out if one of the electronic versions of the Encyclopaedia Britannica is easier to use than the others, to see if the users are satisfied with the information that they retrieved from each version, and to see if the users retrieve the same information from each version. Over one third (six) of 15 participants thought that britannica.com was the easiest to use. The main reason mentioned was the set‐up of the initial results screen. Most of the participants felt that all of the databases answered the searches sufficiently and nine participants felt that britannica.com answered the searches the best. The main reason mentioned was the variety of information sources provided on the search results page (Encyclopaedia Britannica articles, magazine articles, Web sites, and books). Seven of the participants were satisfied with using all of the databases.

scholarly journals Analysis of character network heat based on web retrieval technology

2018 ◽  
Vol 17 ◽  
pp. 03020
Author(s):  
Minglei Wu ◽  
Jingchang Pan
Keyword(s):  
Information Technology ◽  
Search Engine ◽  
Information Needs ◽  
Information Sources ◽  
The Internet ◽  
Amount Of Information ◽  
Search Results ◽  
Web Retrieval ◽  
Information Demand ◽  
Character Network

Information technology is now developing rapidly, the Internet has also obtained widespread popularization. The amount of information on the network is increasing exponentially, whose information sources are widely distributed and varied. If the information can’t be managed in an orderly manner, it will be difficult for the user to extract the information they need from such a massive amount of information. Although the current search engine give people a lot of convenience in searching for information, but the search engine can’t reflect the user’s personalized information demand with facing a wide variety of users with different information needs, knowledge background and interest. In this paper, a recommendation method based on search results with java as a technology is implemented. This method takes Baidu hot people ranking as an example for verifying.

scholarly journals WHAT’S ‘UP NEXT’? INVESTIGATING ALGORITHMIC RECOMMENDATIONS ON YOUTUBE ACROSS ISSUES AND OVER TIME

2021 ◽  
Author(s):  
Ariadna Matamoros-Fernandez ◽  
Joanne Elizabeth Gray ◽  
Louisa Bartolo ◽  
Jean Burgess ◽  
Nicolas Suzor
Keyword(s):  
Qualitative Methods ◽  
Information Sources ◽  
Media Content ◽  
Limited Data ◽  
Policy Changes ◽  
Search Results ◽  
Search Terms ◽  
Over Time ◽  
Sociocultural Issues

YouTube’s ‘up next’ feature algorithmically suggests videos to watch after a video that is currently playing. This feature has been criticised for limiting users’ exposure to diverse media content and information sources; meanwhile, YouTube has reported that they have implemented technical and policy changes to address these concerns. Yet, there is limited data to support either the existing concerns or YouTube’s claims. Drawing on the concept of platform observability, this paper combines computational and qualitative methods to investigate the types of content YouTube’s ‘up next’ feature amplifies over time, using three search terms associated with sociocultural issues where concerns have been raised about YouTube’s role: ‘coronavirus’, ‘feminism’ and ‘beauty’. Over six weeks, we collected the videos (and their metadata) that were highly ranked in the search results for each keyword, as well as the top-ranked recommendations associated with each video, repeating the exercise for three steps in the recommendation chain. We then examined patterns in the recommended videos (and channels) for each query and their variation over time. We found evidence of YouTube's stated efforts to boost ‘authoritative’ media outlets, but at the same time, misleading and controversial content continues to be recommended. We also found that while algorithmic recommendations offer diversity in videos over time, there are clear ‘winners’ at the channel level that are given a visibility boost in YouTube’s 'up next' feature. These impacts were attenuated differently depending on the nature of the search topic.

scholarly journals Fuzzy model of relationship among economic performance, competitiveness and business ethics of small and medium-sized enterprises

2012 ◽  
Vol 60 (4) ◽  
pp. 71-78 ◽  
Author(s):  
Nina Bočková ◽  
Zdeněk Brož ◽  
Mirko Dohnal
Keyword(s):  
Fuzzy Logic ◽  
Business Ethics ◽  
Economic Performance ◽  
Information Sources ◽  
Fuzzy Model ◽  
Fuzzy Reasoning ◽  
Financial Indicators ◽  
Scientific Analysis ◽  
Secondary Information ◽  
South Moravia

The objective of this article is to study the relations among financial indicators, competitiveness and business ethics of comparable small and medium-sized enterprises. A sample of 59 SMEs from the South Moravia region was chosen. All selected companies either produce or service electronics. This research is based on the application of scientific analysis, synthesis, induction, fuzzy logic and modeling. Information for this research was obtained from secondary information sources – Amadeus database, accounting statements and information from the register of companies. Each company is described by a set of 10 variables. Fuzzy sets and reasoning are ideal tools to cope with vague, ill-structured and uncertain scenarios which can be found frequently in business and economics. This is the main reason why fuzzy logic was used in this research. The paper is self-explanatory and no a prior knowledge of fuzzy reasoning is required.

scholarly journals What’s “Up Next”? Investigating Algorithmic Recommendations on YouTube Across Issues and Over Time

2021 ◽  
Vol 9 (4) ◽  
pp. 234-249
Author(s):  
Ariadna Matamoros-Fernández ◽  
Joanne E. Gray ◽  
Louisa Bartolo ◽  
Jean Burgess ◽  
Nicolas Suzor
Keyword(s):  
Qualitative Methods ◽  
Information Sources ◽  
Keyword Search ◽  
Media Content ◽  
Policy Changes ◽  
Search Results ◽  
Search Terms ◽  
The One ◽  
Over Time ◽  
Sociocultural Issues

YouTube’s “up next” feature algorithmically selects, suggests, and displays videos to watch after the one that is currently playing. This feature has been criticized for limiting users’ exposure to a range of diverse media content and information sources; meanwhile, YouTube has reported that they have implemented various technical and policy changes to address these concerns. However, there is little publicly available data to support either the existing concerns or YouTube’s claims of having addressed them. Drawing on the idea of “platform observability,” this article combines computational and qualitative methods to investigate the types of content that the algorithms underpinning YouTube’s “up next” feature amplify over time, using three keyword search terms associated with sociocultural issues where concerns have been raised about YouTube’s role: “coronavirus,” “feminism,” and “beauty.” Over six weeks, we collected the videos (and their metadata, including channel IDs) that were highly ranked in the search results for each keyword, as well as the highly ranked recommendations associated with the videos. We repeated this exercise for three steps in the recommendation chain and then examined patterns in the recommended videos (and the channels that uploaded the videos) for each query and their variation over time. We found evidence of YouTube’s stated efforts to boost “authoritative” media outlets, but at the same time, misleading and controversial content continues to be recommended. We also found that while algorithmic recommendations offer diversity in videos over time, there are clear “winners” at the channel level that are given a visibility boost in YouTube’s “up next” feature. However, these impacts are attenuated differently depending on the nature of the issue.

scholarly journals Search results diversification for effective fair ranking in academic search

2021 ◽  
Author(s):  
Graham McDonald ◽  
Craig Macdonald ◽  
Iadh Ounis
Keyword(s):  
Information Retrieval ◽  
Side Effects ◽  
Information Sources ◽  
Early Career ◽  
Search Results ◽  
Early Career Researchers ◽  
Primary Focus ◽  
Viable Approach ◽  
Fair Ranking ◽  
Academic Search

AbstractProviding users with relevant search results has been the primary focus of information retrieval research. However, focusing on relevance alone can lead to undesirable side effects. For example, small differences between the relevance scores of documents that are ranked by relevance alone can result in large differences in the exposure that the authors of relevant documents receive, i.e., the likelihood that the documents will be seen by searchers. Therefore, developing fair ranking techniques to try to ensure that search results are not dominated, for example, by certain information sources is of growing interest, to mitigate against such biases. In this work, we argue that generating fair rankings can be cast as a search results diversification problem across a number of assumed fairness groups, where groups can represent the demographics or other characteristics of information sources. In the context of academic search, as in the TREC Fair Ranking Track, which aims to be fair to unknown groups of authors, we evaluate three well-known search results diversification approaches from the literature to generate rankings that are fair to multiple assumed fairness groups, e.g. early-career researchers vs. highly-experienced authors. Our experiments on the 2019 and 2020 TREC datasets show that explicit search results diversification is a viable approach for generating effective rankings that are fair to information sources. In particular, we show that building on xQuAD diversification as a fairness component can result in a significant ($$p<0.05$$ p < 0.05 ) increase (up to  50% in our experiments) in the fairness of exposure that authors from unknown protected groups receive.

An Intelligent Metasearch Engine with Link Prediction and Page Clipping Generation Capabilities

2011 ◽  
pp. 199-243
Author(s):  
Cheng-Jye Luh ◽  
Lin-Chih Chen
Keyword(s):  
Search Engines ◽  
User Study ◽  
User Behavior ◽  
Performance Measure ◽  
Search Methods ◽  
Search Results ◽  
General Search ◽  
Metasearch Engine ◽  
Behavior Function ◽  
Browsing Behavior

This chapter presents an intelligent metasearch engine that can recommend a user’s next hyperlink access and relevant paragraphs extracted from metasearch results. The proposed design is based on the primacy effect of browsing behavior, that users prefer top ranking items in search results. Three search methods were implemented in this engine. First, the search engine vector voting (SVV) method rearranges search results gathered from six well-known search engines according to their weights obtained from user behavior function. The hyperlink prediction (HLP) method then arranges the most likely accessed hyperlinks from the URLs in SVV search results. Finally, the page clipping synthesis (PCS) method extracts relevant paragraphs from the HLP search results. A user study indicated that users are more satisfied with the proposed search methods than with general search engines. Moreover, performance measure results confirmed that the proposed search methods outperform other metasearch and search engines.

Migration of European Judicial Ideas Concerning Jurisdiction Over Google on Withdrawal of Information

2016 ◽  
Vol 17 (3) ◽  
pp. 315-337 ◽  
Author(s):  
Krystyna Kowalik-Bańczyk ◽  
Oreste Pollicino
Keyword(s):  
European Union ◽  
Search Engines ◽  
Information Sources ◽  
The European Union ◽  
Information Market ◽  
Search Results ◽  
Court Of Justice

Google's position in the information market has caused interesting legal developments insofar as its obligations are concerned. On various grounds, courts worldwide have begun to impose injunctions on Google that require the company to withdraw the search results—in fact, information sources—that its search engines provide. This article looks at this recent phenomenon of imposing obligations on Google to withdraw some information through the lens of judicial dialogue. In particular, we analyze the “inspiring” role of the Court of Justice of the European Union (CJEU) in itsGoogle Spainjudgment. This case represents a clear migration of some ideas that might be perceived as universal. Some courts outside of Europe—such as Canada—are gaining “inspiration” from the CJEU'sGoogle Spainjudgment in order to reinforce their own decisions. The legitimacy and techniques of this process are also discussed in this article.

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