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ESSAYS IN INTERNET ECONOMICSSHARMA, AMARENDRA KUMAR 15 September 2002 (has links)
No description available.
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WebCrawler : finding what people want /Pinkerton, Brian. January 2000 (has links)
Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (leaves 89-93).
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Meta-search and distributed search systems /Shen, Yipeng. January 2002 (has links)
Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002. / Includes bibliographical references (leaves 138-144). Also available in electronic version. Access restricted to campus users.
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Investigating usability of search engines in small screen devices : a systems engineering approachMoulik, Anand 22 February 2006 (has links)
In today's world, desktop computers have become such an integral part of
our lives that it is practically impossible to imagine anything being done without the
aid of computers. As the world becomes more and more fast paced and users feel a
need to have computers on the go, desktop computers have reduced in size without
compromising on performance. The late 90s saw the desktop segment make room
for the laptop and the small screen devices (SSD) segment, which demonstrated
faster growth rates than the desktop segment. The SSD segment, however, had a
growth rate that was nowhere near the combined growth rate of desktop and laptop
computers. Portability of SSD was one factor that stood out among many others to
account for the unprecedented growth rate of the SSD segment that the computer
industry had witnessed. One of the most important, albeit under-represented and
neglected, factors of a product is its usability. Usability, or the ease with which a
product can be used, can be considered to be one of the most important factors in
the success or failure of product. Determining the usability of small screen devices
presents a bigger challenge, primarily because of the screen size of the SSD. The
process of usability engineering aims to solve some/most of the problems that the
SSD has. To make up for the drawbacks of usability engineering, systems
engineering was used in this thesis, since both disciplines have considerable overlap
in their processes. A growing number of SSD users use the Internet in one form or
the other. The Internet has grown rapidly in the last decade, and nearly everyone
using the Internet has come across a search engine sometime or other. Although
research has been limited to the area of desktop search engines, there has not been
enough research done in the area of search engines for small screen devices. This
thesis compares two different search engines on small screen devices to find the
better between the two. To do so, it takes a close look at the usability engineering
approach from a system engineering perspective revealing several deficiencies,
which may have hitherto gone unnoticed. It also shows a method to integrate several
key Systems Engineering components into the usability engineering approach. / Graduation date: 2006
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On improving the relevancy ranking algorithm in web search engine李莉華, Lee, Lei-wah. January 2000 (has links)
published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy
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On improving the relevancy ranking algorithm in web search engine /Lee, Lei-wah. January 2000 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 78-81).
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Mining user preference using SPY voting for search engine personalization /Deng, Lin. January 2006 (has links)
Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2006. / Includes bibliographical references (leaves 68-73). Also available in electronic version.
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Exploiting the structure of the web for spidering /Young, Joel D. January 2005 (has links)
Thesis (Ph.D.)--Brown University, 2005. / Vita. Thesis advisor: Thomas L. Dean. Includes bibliographical references (leaves 185-191). Also available online.
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Incremental document clustering for web page classification.January 2000 (has links)
by Wong, Wai-Chiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 89-94). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Document Clustering --- p.2 / Chapter 1.2 --- DC-tree --- p.4 / Chapter 1.3 --- Feature Extraction --- p.5 / Chapter 1.4 --- Outline of the Thesis --- p.5 / Chapter 2 --- Related Work --- p.8 / Chapter 2.1 --- Clustering Algorithms --- p.8 / Chapter 2.1.1 --- Partitional Clustering Algorithms --- p.8 / Chapter 2.1.2 --- Hierarchical Clustering Algorithms --- p.10 / Chapter 2.2 --- Document Classification by Examples --- p.11 / Chapter 2.2.1 --- k-NN algorithm - Expert Network (ExpNet) --- p.11 / Chapter 2.2.2 --- Learning Linear Text Classifier --- p.12 / Chapter 2.2.3 --- Generalized Instance Set (GIS) algorithm --- p.12 / Chapter 2.3 --- Document Clustering --- p.13 / Chapter 2.3.1 --- B+-tree-based Document Clustering --- p.13 / Chapter 2.3.2 --- Suffix Tree Clustering --- p.14 / Chapter 2.3.3 --- Association Rule Hypergraph Partitioning Algorithm --- p.15 / Chapter 2.3.4 --- Principal Component Divisive Partitioning --- p.17 / Chapter 2.4 --- Projections for Efficient Document Clustering --- p.18 / Chapter 3 --- Background --- p.21 / Chapter 3.1 --- Document Preprocessing --- p.21 / Chapter 3.1.1 --- Elimination of Stopwords --- p.22 / Chapter 3.1.2 --- Stemming Technique --- p.22 / Chapter 3.2 --- Problem Modeling --- p.23 / Chapter 3.2.1 --- Basic Concepts --- p.23 / Chapter 3.2.2 --- Vector Model --- p.24 / Chapter 3.3 --- Feature Selection Scheme --- p.25 / Chapter 3.4 --- Similarity Model --- p.27 / Chapter 3.5 --- Evaluation Techniques --- p.29 / Chapter 4 --- Feature Extraction and Weighting --- p.31 / Chapter 4.1 --- Statistical Analysis of the Words in the Web Domain --- p.31 / Chapter 4.2 --- Zipf's Law --- p.33 / Chapter 4.3 --- Traditional Methods --- p.36 / Chapter 4.4 --- The Proposed Method --- p.38 / Chapter 4.5 --- Experimental Results --- p.40 / Chapter 4.5.1 --- Synthetic Data Generation --- p.40 / Chapter 4.5.2 --- Real Data Source --- p.41 / Chapter 4.5.3 --- Coverage --- p.41 / Chapter 4.5.4 --- Clustering Quality --- p.43 / Chapter 4.5.5 --- Binary Weight vs Numerical Weight --- p.45 / Chapter 5 --- Web Document Clustering Using DC-tree --- p.48 / Chapter 5.1 --- Document Representation --- p.48 / Chapter 5.2 --- Document Cluster (DC) --- p.49 / Chapter 5.3 --- DC-tree --- p.52 / Chapter 5.3.1 --- Tree Definition --- p.52 / Chapter 5.3.2 --- Insertion --- p.54 / Chapter 5.3.3 --- Node Splitting --- p.55 / Chapter 5.3.4 --- Deletion and Node Merging --- p.56 / Chapter 5.4 --- The Overall Strategy --- p.57 / Chapter 5.4.1 --- Preprocessing --- p.57 / Chapter 5.4.2 --- Building DC-tree --- p.59 / Chapter 5.4.3 --- Identifying the Interesting Clusters --- p.60 / Chapter 5.5 --- Experimental Results --- p.61 / Chapter 5.5.1 --- Alternative Similarity Measurement : Synthetic Data --- p.61 / Chapter 5.5.2 --- DC-tree Characteristics : Synthetic Data --- p.63 / Chapter 5.5.3 --- Compare DC-tree and B+-tree: Synthetic Data --- p.64 / Chapter 5.5.4 --- Compare DC-tree and B+-tree: Real Data --- p.66 / Chapter 5.5.5 --- Varying the Number of Features : Synthetic Data --- p.67 / Chapter 5.5.6 --- Non-Correlated Topic Web Page Collection: Real Data --- p.69 / Chapter 5.5.7 --- Correlated Topic Web Page Collection: Real Data --- p.71 / Chapter 5.5.8 --- Incremental updates on Real Data Set --- p.72 / Chapter 5.5.9 --- Comparison with the other clustering algorithms --- p.73 / Chapter 6 --- Conclusion --- p.75 / Appendix --- p.77 / Chapter A --- Stopword List --- p.77 / Chapter B --- Porter's Stemming Algorithm --- p.81 / Chapter C --- Insertion Algorithm --- p.83 / Chapter D --- Node Splitting Algorithm --- p.85 / Chapter E --- Features Extracted in Experiment 4.53 --- p.87 / Bibliography --- p.88
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Enhancing information retrieval effectiveness through use of contextChanana, Vivek, University of Western Sydney, College of Science, Technology and Environment, School of Computing and Information Technology January 2004 (has links)
Information available in digital form has grown phenomenally in recent years. Finding the required information has become a difficult and challenging task. This is primarily due to the diversity and enormous volume of information available and the change in the nature of people now seeking information – from experts to ordinary users of desktop computers with varying interest and objectives. The problem of finding relevant information is further impacted by the poor retrieval effectiveness of most current information retrieval (IR) systems that are primarily based on keyword indexing techniques. Though these systems retrieve documents that contain those keywords specified in the query, the documents that are retrieved may not necessarily be in the context in which the user would have wanted them to be. This research works argues that exploiting the user’s context of the information need has the potential to improve the performance of information retrieval systems. Context can reduce the ambiguity by associating meanings to request/query terms, and thus limit the scope of the possible misinterpretations of query terms. A new way of defining context categories based on information type is proposed and this notion of context differs from the conventional way of defining information categories based on subject topics as it is closely linked with the situation in which the user’s needs for information originates. A new context-based information retrieval system where users could specify the context in which they are seeking information is presented. This work also includes a full-scale development, implementation and evaluation of the new context-based information system / Doctor of Philosophy (PhD)
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