Mental Models and Information Retrieval: What Can Search Queries Tell Us?

2013 ◽  
Author(s):  
Haidar Moukdad ◽  
Andrew Large
Keyword(s):  
Information Retrieval ◽  
Mental Models ◽  
Search Engine ◽  
Mental Model ◽  
Web Search ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Search Queries ◽  
Web Search Engine ◽  
Advanced Search

When information seekers use an information retrieval system their strategy is based, at least in part, on the mental model they have constructed about this environment. A random sample was gathered of more than 2000 actual search queries submitted by users to one web search engine. WebCrawler, in two separate capture sessions. The results suggest that a high proportion of users do not employ advanced search features...

Doctoral Students’ Mental Models of a Web Search Engine

2013 ◽  
Author(s):  
Ping Li ◽  
Jamshid Beheshti
Keyword(s):  
Pilot Study ◽  
Mental Models ◽  
Doctoral Students ◽  
Search Engine ◽  
Mental Model ◽  
Web Search ◽  
Model Completeness ◽  
Web Search Engine

This paper reports on the first stage of a research on doctoral students’ mental models of a Web search engine and factors that may affect their mental models. A modified version of a mental model completeness scale was developed and tested in a pilot study in Web search engine context.Cet article présente le premier stade d'une recherche sur les modèles mentaux des étudiants doctoraux avec un moteur de recherche Web et les facteurs qui peuvent les affecter. Une version modifiée d’une échelle de la perfection du modèle mental a été développée et examinée dans le contexte d’une étude préliminaire effectuée avec un moteur de recherche Web. 

Information Retrieval

2016 ◽  
Author(s):  
Qiaozhu Mei ◽  
Dragomir Radev
Keyword(s):  
Information Retrieval ◽  
Digital Libraries ◽  
Web Search ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Information Need ◽  
System A ◽  
Recent Developments ◽  
Text Information ◽  
Text Information Retrieval

This chapter is a basic introduction to text information retrieval. Information Retrieval (IR) refers to the activities of obtaining information resources (usually in the form of textual documents) from a much larger collection, which are relevant to an information need of the user (usually expressed as a query). Practical instances of an IR system include digital libraries and Web search engines. This chapter presents the typical architecture of an IR system, an overview of the methods corresponding to the design and the implementation of each major component of an information retrieval system, a discussion of evaluation methods for an IR system, and finally a summary of recent developments and research trends in the field of information retrieval.

Associating Searching on Search Engines to Subsequent Searching on Sites

2016 ◽  
Vol 8 (2) ◽  
pp. 30-43
Author(s):  
Adan Ortiz-Cordova ◽  
Bernard J. Jansen
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Research Study ◽  
Search Queries ◽  
Web Search Engine ◽  
Search Patterns ◽  
Search Information

In this research study, the authors investigate the association between external searching, which is searching on a web search engine, and internal searching, which is searching on a website. They classify 295,571 external – internal searches where each search is composed of a search engine query that is submitted to a web search engine and then one or more subsequent queries submitted to a commercial website by the same user. The authors examine 891,453 queries from all searches, of which 295,571 were external search queries and 595,882 were internal search queries. They algorithmically classify all queries into states, and then clustered the searching episodes into major searching configurations and identify the most commonly occurring search patterns for both external, internal, and external-to-internal searching episodes. The research implications of this study are that external sessions and internal sessions must be considered as part of a continuous search episode and that online businesses can leverage external search information to more effectively target potential consumers.

scholarly journals Sentence Classification and Information Retrieval for Petroleum Engineering

2019 ◽  
Author(s):  
Thiago Ferraz ◽  
Gabriel Ferreira ◽  
Fábio Cozman ◽  
Ismael Santos
Keyword(s):  
Logistic Regression ◽  
Information Retrieval ◽  
Text Mining ◽  
Search Engine ◽  
Classification System ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Petroleum Engineering ◽  
Testing Data ◽  
Sentence Classification

Classifying sentences in industrial, technical or scientific reports can enhance text mining and information retrieval tasks with useful machinereadable metadata. This paper describes a search engine that employs sentence classification so as to search for abstracts from scholarly papers in Petroleum Engineering. The sentences were classified into four classes, based on the popular IMRAD categories. We produced a dataset containing more than 2,200 manually labeled sentences from 278 scholarly articles in the field of Petroleum Engineering in order to be used as training and testing data. The classifier with best results was logistic regression, with an accuracy of 86.4%. The information retrieval system built on top of the classification system yielded a mAP of 0.80.

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. 

Users’ Click and Bookmark Based Personalization Using Modified Agglomerative Clustering for Web Search Engine

2017 ◽  
Vol 26 (06) ◽  
pp. 1730002 ◽  
Author(s):  
T. Dhiliphan Rajkumar ◽  
S. P. Raja ◽  
A. Suruliandi
Keyword(s):  
Information Retrieval ◽  
Search Engine ◽  
Search Engines ◽  
Web Search ◽  
Irrelevant Information ◽  
Experimental Results ◽  
Agglomerative Clustering ◽  
Semantic Level ◽  
Web Search Engine ◽  
The Web

Short and ambiguous queries are the major problems in search engines which lead to irrelevant information retrieval for the users’ input. The increasing nature of the information on the web also makes various difficulties for the search engine to provide the users needed results. The web search engine experience the ill effects of ambiguity, since the queries are looked at on a rational level rather than the semantic level. In this paper, for improving the performance of search engine as of the users’ interest, personalization is based on the users’ clicks and bookmarking is proposed. Modified agglomerative clustering is used in this work for clustering the results. The experimental results prove that the proposed work scores better precision, recall and F-score.

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