A Case Study of Using Web Search Statistics: Case Restoration

2010 ◽  
pp. 199-211 ◽  
Author(s):  
Silviu Cucerzan
Keyword(s):  
Web Search

A Correlation Coupling Prediction of Island Tourist Based on Multi Key Words of Web Search Index: A Case Study of Gulang Island in Xiamen

2020 ◽  
Vol 115 (sp1) ◽  
pp. 373
Author(s):  
Yuan He ◽  
Lingying Huang ◽  
Can Ding ◽  
Yue Zou ◽  
Ping Huang
Keyword(s):  
Key Words ◽  
Web Search ◽  
Search Index

Strategies for Improving the Efficacy of Fusion Question Answering Systems

2012 ◽  
pp. 181-198
Author(s):  
José Antonio Robles-Flores ◽  
Gregory Schymik ◽  
Julie Smith-David ◽  
Robert St. Louis
Keyword(s):  
Search Engines ◽  
Web Site ◽  
Web Search ◽  
Question Answering ◽  
Irrelevant Information ◽  
Web Pages ◽  
Crowd Sourcing ◽  
Complete Answer ◽  
Question Answering Systems

Web search engines typically retrieve a large number of web pages and overload business analysts with irrelevant information. One approach that has been proposed for overcoming some of these problems is automated Question Answering (QA). This paper describes a case study that was designed to determine the efficacy of QA systems for generating answers to original, fusion, list questions (questions that have not previously been asked and answered, questions for which the answer cannot be found on a single web site, and questions for which the answer is a list of items). Results indicate that QA algorithms are not very good at producing complete answer lists and that searchers are not very good at constructing answer lists from snippets. These findings indicate a need for QA research to focus on crowd sourcing answer lists and improving output format.

Factors Affecting Users’ Mental Models of a Web Search Engine: A Case Study

2013 ◽  
Author(s):  
Li Ping ◽  
Jamshid Beheshti.
Keyword(s):  
Mental Models ◽  
Doctoral Students ◽  
Search Engine ◽  
Web Search ◽  
Search Performance ◽  
User Group ◽  
Factors Affecting ◽  
Web Search Engine ◽  
Collecte De Données

Focusing on doctoral students as a specific user group, for a case study, this research investigates factors that might affect users’ mental models of a Web search engine measured in the dimension of completeness, and subsequently on their search performance. Data collection techniques include interview, observation and four standard tests.Se basant sur les étudiants au doctorat comme groupe spécifique d’utilisateurs pour réaliser une étude de cas, cette recherche examine les facteurs pouvant affecter les modèles mentaux des utilisateurs d’un moteur de recherche web, mesurés en termes d’exhaustivité, et subséquemment en termes de performance de recherche. Les techniques de collecte de données incluent l’entrevue, l’observation et quatre tests standardisés. 

scholarly journals WEB SEARCH BEHAVIOUR OF RESEARCH SCHOLARS: A CASE STUDY 

2018 ◽  
Vol 7 (2) ◽  
Author(s):  
TINJU TOM ◽  
KAVITHA KAMAL RAJ ◽  
SUPRIYA SUSAN KURIAN ◽  
CHIKKU BALACHANDRAN
Keyword(s):  
Web Search ◽  
Search Behaviour

A Systematic Literature Review Of Web Search Personalization

2020 ◽  
Vol 13 ◽  
Author(s):  
Sunny Sharma ◽  
Vijay Rana
Keyword(s):  
Literature Review ◽  
Search Engines ◽  
Web Search ◽  
Geographical Location ◽  
Future Research ◽  
Research Directions ◽  
Search Results ◽  
Future Research Directions ◽  
The Web

: The Existing studies have already revealed that the information on the web is increasing rapidly. Ambiguous queries and user’s ability to express their intention through queries have been one of the key challenges in retrieving the accurate search results from the search engine. This paper in response explored different methodologies proposed during 2005-2019 by the eminent researchers for recommending better search results. Some of these methodologies are based on the users’ geographical location while others rely on re- rank the web results and refinement of user’s query. Fellow researchers can use this literature, to define the fundamental literature for their own work. Further a brief case study of major search engines like Google, Yahoo, Bing etc. along with the techniques used by these search engines for personalization are also discussed. Finally, the paper discusses some current issues and challenges related to the personalization which further lays the future research directions.

Strategies for Improving the Efficacy of Fusion Question Answering Systems

2011 ◽  
Vol 2 (1) ◽  
pp. 46-63
Author(s):  
José Antonio Robles-Flores ◽  
Gregory Schymik ◽  
Julie Smith-David ◽  
Robert St. Louis
Keyword(s):  
Search Engines ◽  
Web Site ◽  
Web Search ◽  
Question Answering ◽  
Irrelevant Information ◽  
Web Pages ◽  
Crowd Sourcing ◽  
Complete Answer ◽  
Question Answering Systems

Web search engines typically retrieve a large number of web pages and overload business analysts with irrelevant information. One approach that has been proposed for overcoming some of these problems is automated Question Answering (QA). This paper describes a case study that was designed to determine the efficacy of QA systems for generating answers to original, fusion, list questions (questions that have not previously been asked and answered, questions for which the answer cannot be found on a single web site, and questions for which the answer is a list of items). Results indicate that QA algorithms are not very good at producing complete answer lists and that searchers are not very good at constructing answer lists from snippets. These findings indicate a need for QA research to focus on crowd sourcing answer lists and improving output format.

scholarly journals An information search model for online social Networks - MOBIRSE

2015 ◽  
Vol 35 (3) ◽  
pp. 76-83
Author(s):  
Miguel Angel Niño Zambrano ◽  
Iván Darío Cerón Moreno ◽  
Jhon Alberto Astaiza Perafán ◽  
Gustavo Adolfo Ramírez
Keyword(s):  
Social Networks ◽  
Information Search ◽  
Online Social Networks ◽  
Web Search ◽  
User Profile ◽  
Kappa Statistics ◽  
Internet Users ◽  
Proposed Model ◽  
The Social

Online Social Networks (OSNs) have been gaining great importance among Internet users in recent years.  These are sites where it is possible to meet people, publish, and share content in a way that is both easy and free of charge. As a result, the volume of information contained in these websites has grown exponentially, and web search has consequently become an important tool for users to easily find information relevant to their social networking objectives. Making use of ontologies and user profiles can make these searches more effective. This article presents a model for Information Retrieval in OSNs (MOBIRSE) based on user profile and ontologies which aims to improve the relevance of retrieved information on these websites. The social network Facebook was chosen for a case study and as the instance for the proposed model. The model was validated using measures such as At-k Precision and Kappa statistics, to assess its efficiency.

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