Search results presentation and interface design: A comparative evaluation study of five web search engines in Arabic language

2010 ◽  
Author(s):  
Wissam Tawileh ◽  
Thomas Mandl ◽  
Joachim Griesbaum
Keyword(s):  
Search Engines ◽  
Interface Design ◽  
Comparative Evaluation ◽  
Web Search ◽  
Evaluation Study ◽  
Arabic Language ◽  
Search Results ◽  
Web Search Engines

A Study on Web Searching

2004 ◽  
pp. 208-225
Author(s):  
Shanfeng Zhu ◽  
Xiaotie Deng ◽  
Qizhi Fang ◽  
Weimin Zhang
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Experimental Results ◽  
Web Searching ◽  
Search Results ◽  
Total Index ◽  
Depth Study ◽  
Web Search Engines ◽  
The Web

Web search engines are one of the most popular services to help users find useful information on the Web. Although many studies have been carried out to estimate the size and overlap of the general web search engines, it may not benefit the ordinary web searching users, since they care more about the overlap of the top N (N=10, 20 or 50) search results on concrete queries, but not the overlap of the total index database. In this study, we present experimental results on the comparison of the overlap of the top N (N=10, 20 or 50) search results from AlltheWeb, Google, AltaVista and WiseNut for the 58 most popular queries, as well as for the distance of the overlapped results. These 58 queries are chosen from WordTracker service, which records the most popular queries submitted to some famous metasearch engines, such as MetaCrawler and Dogpile. We divide these 58 queries into three categories for further investigation. Through in-depth study, we observe a number of interesting results: the overlap of the top N results retrieved by different search engines is very small; the search results of the queries in different categories behave in dramatically different ways; Google, on average, has the highest overlap among these four search engines; each search engine tends to adopt a different rank algorithm independently.

scholarly journals Credibility in Web Search Engines

2012 ◽  
pp. 131-146 ◽  
Author(s):  
Dirk Lewandowski
Keyword(s):  
User Satisfaction ◽  
Search Engines ◽  
Web Search ◽  
Broad Context ◽  
Fully Integrated ◽  
Search Results ◽  
Explicit Measure ◽  
Web Search Engines ◽  
Explicit Measures

Web search engines apply a variety of ranking signals to achieve user satisfaction, i.e., results pages that provide the best-possible results for the user. While these ranking signals implicitly consider credibility (e.g., by measuring popularity), explicit measures of credibility are not applied. In this chapter, credibility in Web search engines is discussed in a broad context: credibility as a measure for including documents in a search engine’s index, credibility as a ranking signal, credibility in the context of universal search results, and the possibility of using credibility as an explicit measure for ranking purposes. It is found that while search engines—at least to a certain extent—show credible results to their users, there is no fully integrated credibility framework for Web search engines.

Searching web documents using a summarization approach

2016 ◽  
Vol 12 (1) ◽  
pp. 83-101 ◽  
Author(s):  
Rani Qumsiyeh ◽  
Yiu-Kai Ng
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Specific Information ◽  
Information Need ◽  
Search Query ◽  
Content Type ◽  
Additional Information ◽  
Search Results ◽  
User Query ◽  
Web Search Engines

Purpose The purpose of this paper is to introduce a summarization method to enhance the current web-search approaches by offering a summary of each clustered set of web-search results with contents addressing the same topic, which should allow the user to quickly identify the information covered in the clustered search results. Web search engines, such as Google, Bing and Yahoo!, rank the set of documents S retrieved in response to a user query and represent each document D in S using a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e. assisting its users to quickly identify results of interest. These snippets are inadequate in providing distinct information and capture the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user’s intended request without requiring additional information. Furthermore, a document title is not always a good indicator of the content of the corresponding document either. Design/methodology/approach The authors propose to develop a query-based summarizer, called QSum, in solving the existing problems of Web search engines which use titles and abstracts in capturing the contents of retrieved documents. QSum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user’s time and effort in searching for specific information of interest by skipping the step to browse through the retrieved documents one by one. Findings Experimental results show that QSum is effective and efficient in creating a high-quality summary for each cluster to enhance Web search. Originality/value The proposed query-based summarizer, QSum, is unique based on its searching approach. QSum is also a significant contribution to the Web search community, as it handles the ambiguous problem of a search query by creating summaries in response to different interpretations of the search which offer a “road map” to assist users to quickly identify information of interest.

Context-Aware Personalized Web Search Using Navigation History

2020 ◽  
Vol 16 (2) ◽  
pp. 91-107
Author(s):  
Wiem Chebil ◽  
Mohammad O Wedyan ◽  
Haiyan Lu ◽  
Omar Ghaleb Elshaweesh
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Promising Result ◽  
Semantic Knowledge ◽  
Great Effort ◽  
User Profiles ◽  
New Techniques ◽  
Search Results ◽  
Web Search Engines ◽  
Google Search

It is highly desirable that web search engines know users well and provide just what the user needs. Although great effort has been devoted to achieve this dream, the commonly used web search engines still provide a “one-fit-all” results. One of the barriers is lack of an accurate representation of user search context that supports personalised web search. This article presents a method to represent user search context and incorporate this representation to produce personalised web search results based on Google search results. The key contributions are twofold: a method to build contextual user profiles using their browsing behaviour and the semantic knowledge represented in a domain ontology; and an algorithm to re-rank the original search results using these contextual user profiles. The effectiveness of proposed new techniques were evaluated through comparisons of cases with and without these techniques respectively and a promising result of 35% precision improvement is achieved.

A Study on Web Searching

2008 ◽  
pp. 1926-1937
Author(s):  
Shanfeng Chu ◽  
Xiaotie Deng ◽  
Qizhi Fang ◽  
Weimin Zhang
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Experimental Results ◽  
Web Searching ◽  
Search Results ◽  
Total Index ◽  
Depth Study ◽  
Web Search Engines ◽  
The Web

Web search engines are one of the most popular services to help users find useful information on the Web. Although many studies have been carried out to estimate the size and overlap of the general web search engines, it may not benefit the ordinary web searching users, since they care more about the overlap of the top N (N=10, 20 or 50) search results on concrete queries, but not the overlap of the total index database. In this study, we present experimental results on the comparison of the overlap of the top N (N=10, 20 or 50) search results from AlltheWeb, Google, AltaVista and WiseNut for the 58 most popular queries, as well as for the distance of the overlapped results. These 58 queries are chosen from WordTracker service, which records the most popular queries submitted to some famous metasearch engines, such as MetaCrawler and Dogpile. We divide these 58 queries into three categories for further investigation. Through in-depth study, we observe a number of interesting results: the overlap of the top N results retrieved by different search engines is very small; the search results of the queries in different categories behave in dramatically different ways; Google, on average, has the highest overlap among these four search engines; each search engine tends to adopt a different rank algorithm independently.

Cluster Search Engine Results with Crowd Intelligence

2013 ◽  
Vol 284-287 ◽  
pp. 3375-3379
Author(s):  
Chun Hsiung Tseng ◽  
Fu Cheng Yang ◽  
Yu Ping Tseng ◽  
Yi Yun Chang
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Web Pages ◽  
Huge Amount ◽  
Search Results ◽  
Web Search Engines ◽  
Crowd Intelligence ◽  
User Queries

Most Web users today rely heavily on search engines to gather information. To achieve better search results, some algorithms such as PageRank have been developed. However, most Web search engines employ keyword-based search and thus have some natural weaknesses. Among these problems, a well-known one is that it is very difficult for search engines to infer semantics from user queries and returned results. Hence, despite of efforts of ranking search results, users may still have to navigate through a huge amount of Web pages to locate the desired resources. In this research, the researchers developed a clustering-based methodology to improve the performance of search engines. Instead of extracting features used for clustering from the returned documents, the proposed method extracts features from the delicious service, which is actually a tag provider service. By utilizing such information, the resulting system can benefit from crowd intelligence. The obtained information is then used for enhancing the performance of the ordinary k-means algorithm to achieve better clustering results.

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