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E-raamat: Web Search Engine Research

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"Web Search Engine Research", edited by Dirk Lewandowski, provides an understanding of Web search engines from the unique perspective of Library and Information Science. The book explores a range of topics including retrieval effectiveness, user satisfaction, the evaluation of search interfaces, the impact of search on society, reliability of search results, query log analysis, user guidance in the search process, and the influence of search engine optimization (SEO) on results quality. While research in computer science has mainly focused on technical aspects of search engines, LIS research is centred on users' behaviour when using search engines and how this interaction can be evaluated. LIS research provides a unique perspective in intermediating between the technical aspects, user aspects and their impact on their role in knowledge acquisition. This book is directly relevant to researchers and practitioners in library and information science, computer science, including Web researchers.

Provides an understanding of Web search engines from the unique perspective of Library and Information Science. This book explores a range of topics including retrieval effectiveness, user satisfaction, the evaluation of search interfaces, the impact of search on society, and the influence of search engine optimization (SEO) on results quality.

List of Contributors
xi
Editorial Advisory Board xiii
1 New Perspectives on Web Search Engine Research
1(18)
Dirk Lewandowski
1.1 The Context of Web Search Engine Research
2(4)
1.1.1 The Search Engine Market
2(1)
1.1.2 Challenges to Information Retrieval and the Library and Information Science Research Communities
3(1)
1.1.3 Approaches to Classifying Web Search Engine Research Areas
4(2)
1.2 Book Outline
6(6)
1.2.1 Part 1: Emerging Areas of Web Searching
6(2)
1.2.2 Part 2: Beyond Traditional Search Engine Evaluation
8(2)
1.2.3 Part 3: New Perspectives on Web Searching
10(2)
1.3 Suggestions for Further Research
12(7)
References
14(5)
PART 1 EMERGING AREAS OF WEB SEARCHING
2 The Many Ways of Searching the Web Together: A Comparison of Social Search Engines
19(28)
Manuel Burghardt
Markus Heckner
Christian Wolff
2.1 Introduction
20(1)
2.2 What Is Social Search?
21(5)
2.2.1 Context and History of Social Information Retrieval
21(1)
2.2.2 The Social Graph
22(1)
2.2.3 Defining Social Search
23(3)
2.3 Social Tagging Systems
26(3)
2.3.1 Fundamentals and Motivation for Social Tagging
27(1)
2.3.2 Direct Usage of Social Tagging Systems
28(1)
2.3.3 Indirect Usage of Social Tags as Input for Search Algorithms
29(1)
2.4 Social Question-Answering
29(5)
2.4.1 Fundamentals of Social QA
29(3)
2.4.2 Community-Based Systems
32(1)
2.4.3 Expert-Based Systems
32(2)
2.5 Collaborative Search
34(1)
2.6 Collaborative Filtering and Recommender Services
35(3)
2.7 Personalized Social Search
38(2)
2.8 Outlook
40(7)
References
41(6)
3 Local Web Search Examined
47(32)
Dirk Ahlers
3.1 Introduction
48(2)
3.2 History and Overview
50(5)
3.2.1 Local Search
50(3)
3.2.2 Geographic Information Retrieval
53(1)
3.2.3 Search Scenarios and Information Needs
53(2)
3.3 Current Local Search Engines
55(18)
3.3.1 Yellow Pages
57(1)
3.3.2 Map-Based Local Web Search
58(7)
3.3.3 General Web Search
65(6)
3.3.4 Other Local Search Services
71(1)
3.3.5 Impact
72(1)
3.4 Mobile Local Search
73(1)
3.5 Future Research and Evolution of Local Search
74(5)
References
74(5)
4 The Computational Analysis of Web Search Statistics in the Intelligent Framework Supporting Decision Making
79(26)
Wieslaw Pietruszkiewicz
4.1 Introduction
80(2)
4.2 Web Search Queries Statistics
82(3)
4.3 Scientific Basics of Prognosis and Forecasting Based on Web Search Statistics
85(2)
4.4 Examples of Web Search Statistics Analyses
87(4)
4.5 Issues of Search Queries Computational Processing
91(2)
4.6 Associative Analysis of Search Queries Statistics
93(1)
4.7 WebPerceiver Platform
94(5)
4.8 Practical Implications and Further Development and Research
99(1)
4.9 Summary and Conclusions
100(5)
References
101(4)
PART 2 BEYOND TRADITIONAL SEARCH ENGINE EVALUATION
5 Evaluating Web Retrieval Effectiveness
105(34)
Ben Carterette
Evangelos Kanoulas
Emine Yilmaz
5.1 Introduction
106(1)
5.2 Test Collections
107(4)
5.2.1 Assembling a Document Collection
108(1)
5.2.2 Creating a Set of Information Needs
108(1)
5.2.3 Identifying the Set of Documents Relevant to the Topics
109(2)
5.3 Traditional Evaluation Measures
111(3)
5.3.1 Precision and Recall
111(2)
5.3.2 Average Precision
113(1)
5.4 Evaluation with User Models
114(10)
5.4.1 Cooper's Expected Search Length
114(1)
5.4.2 Robertson's Interpretation of Average Precision
115(1)
5.4.3 Graded Relevance
116(1)
5.4.4 Rank-Biased Precision
117(1)
5.4.5 Normalized Discounted Cumulative Gain
118(3)
5.4.6 Expected Reciprocal Rank
121(1)
5.4.7 Expected Browsing Utility
122(2)
5.5 Advanced User Models
124(9)
5.5.1 Novelty and Diversity
124(5)
5.5.2 Sessions
129(4)
5.6 Online Evaluation of Retrieval Systems
133(1)
5.7 Conclusion
134(5)
References
135(4)
6 Diversity-Aware Search: New Possibilities and Challenges for Web Search
139(24)
Kerstin Denecke
6.1 Introduction
140(1)
6.2 Diversity and Its Notions
141(5)
6.3 Diversity in Web Search
146(7)
6.3.1 Diversity in Result Sets and Ranking
147(2)
6.3.2 Comprehensive Diversity Analysis
149(2)
6.3.3 Evaluation Measures for Result Diversification
151(2)
6.4 Diversity-Aware Search Engine for Medical Web Content: An Example
153(6)
6.4.1 Functionality of the Search Engine
153(2)
6.4.2 System Architecture and Diversity Analysis Strategy
155(3)
6.4.3 Transfer to Other Domains
158(1)
6.5 Future Challenges
159(4)
References
160(3)
7 Personalised Search Engine Evaluation: Methodologies and Metrics
163(40)
Kin Fun Li
Yali Wang
Wei Yu
7.1 Introduction and Motivation
164(1)
7.2 Search Engine Evaluation Criteria
165(3)
7.2.1 User-Oriented Evaluation
167(1)
7.3 A Search Engine Evaluation Model
168(7)
7.3.1 Weighted Parameters and Summary Score
169(1)
7.3.2 Feature Parameters
170(2)
7.3.3 Performance Parameters
172(1)
7.3.4 Evaluating Relevance Using a Common List
173(1)
7.3.5 More on Quality Issues
174(1)
7.4 Search Engine Comparison Using the Evaluation Model
175(2)
7.4.1 Data Collection Methodology
175(1)
7.4.2 Qualitative and Quantitative Evaluations
175(1)
7.4.3 An Illustrative Case Study
176(1)
7.5 Performance Measurement Metrics
177(3)
7.5.1 Frequency Measures
179(1)
7.6 Histogram Patterns and Classification
180(3)
7.6.1 Daily Duplication Frequency Patterns
181(2)
7.7 Period Duplication Frequency Patterns
183(3)
7.7.1 Daily Rank Change Frequency Patterns
183(1)
7.7.2 Remarks on Patterns
183(3)
7.8 Comparing and Classifying Histogram Patterns
186(4)
7.8.1 Parameters for Histogram Patterns
187(1)
7.8.2 Expected Value as a Single Index
187(3)
7.9 Validation of the Proposed Pattern Classification
190(3)
7.9.1 Case I
190(3)
7.9.2 Case II
193(1)
7.10 Conclusions
193(10)
References
194(9)
8 Search Engines and Rank Correlation
203(24)
Massimo Melucci
8.1 Introduction
204(1)
8.2 Motivations and Concepts
205(7)
8.3 Literature Survey
212(3)
8.3.1 Document Ranking
212(1)
8.3.2 System Ranking
213(1)
8.3.3 System Architecture
214(1)
8.4 Discussion
215(2)
8.5 Alternative RCCs
217(2)
8.6 A Framework for Investigating Rank Correlation
219(2)
8.7 Conclusions
221(6)
References
222(5)
PART 3 NEW PERSPECTIVES ON WEB SEARCHING
9 Beyond Search: A Technology Probe Investigation
227(24)
Erin M. Bryant
Richard Harper
Philip Gosset
9.1 Typologies of Web Use
228(5)
9.2 Designing Web Interaction Probes
233(5)
9.2.1 Cards
235(2)
9.2.2 Pebbles
237(1)
9.3 Field Trial
238(2)
9.4 Findings
240(6)
9.4.1 Confounding Properties of the Experience
240(2)
9.4.2 Grasping the Possibilities of Pebbles
242(3)
9.4.3 Reflections on Cards
245(1)
9.5 Conclusions and Implications for Future Research
246(5)
References
248(3)
10 How Search Engine Users Evaluate and Select Web Search Results: The Impact of the Search Engine Interface on Credibility Assessments
251(30)
Yvonne Kammerer
Peter Gerjets
10.1 Introduction and Overview
252(2)
10.2 The Importance of Information Quality and Credibility on the Web
254(2)
10.2.1 Definitions of Information Quality and Credibility
254(1)
10.2.2 The Heterogeneity of Information Sources on the Web and in Search Engine Lists
254(2)
10.3 Selection of Search Results from SERPs
256(3)
10.3.1 Information Foraging Theory: Search Result Selection Based on Information Scent and Satisficing Strategies
256(1)
10.3.2 Empirical Findings About Laypeople's Search Result Selection Behavior
257(2)
10.4 Credibility Assessments on the Web
259(3)
10.4.1 Prominence-Interpretation Theory: Two Constituent Components for Credibility Assessments
259(1)
10.4.2 Potential Reasons for the Neglect of Credibility of Information Sources on SERPs
260(2)
10.5 Alternative Search Engine Interfaces to Support Credibility Assessments
262(10)
10.5.1 Reducing the Prominence of the Search Results Ranking
262(3)
10.5.2 Increasing the Prominence of Quality-Related Cues on SERPs
265(4)
10.5.3 Automatic Classification of Search Results According to Genre Categories
269(3)
10.6 Conclusions
272(9)
References
275(6)
11 What Would Kant Think? Testing Truth Claims in Research Traditions, and Proposing Deeper Meanings for the Concept of "Search"
281(28)
Denise N. Rall
11.1 Introduction
282(3)
11.2 Truth Claims in Science
285(3)
11.3 Truth Claims in Social Science
288(2)
11.4 Truth Claims in Law
290(2)
11.5 Judgmental Truth Claims
292(2)
11.6 Types and Behaviors of Networks
294(3)
11.7 Search Engine Mechanisms
297(1)
11.8 Considering Other Philosophies in the Meanings of Search
298(1)
11.9 The Index as an Aesthetic Marker of Presence
298(2)
11.10 The Psychological Basis of Search
300(1)
11.11 Performativity
301(2)
11.12 Conclusion
303(6)
References
304(5)
About the Authors 309(6)
Subject Index 315
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