Cheng Tung Yin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 136-141). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.1.1 --- Information Retrieval --- p.1 / Chapter 1.1.2 --- Search Engines --- p.2 / Chapter 1.1.3 --- Data Merging --- p.3 / Chapter 1.2 --- Meta-search Engines --- p.3 / Chapter 1.2.1 --- Framework and Techniques Employed --- p.3 / Chapter 1.2.2 --- Advantages of meta-searching --- p.8 / Chapter 1.3 --- Contribution of the Thesis --- p.10 / Chapter 1.4 --- Organization of the Thesis --- p.12 / Chapter 2 --- Literature Review --- p.14 / Chapter 2.1 --- Preliminaries --- p.14 / Chapter 2.2 --- Fusion Methods --- p.15 / Chapter 2.2.1 --- Fusion methods based on a document's score --- p.15 / Chapter 2.2.2 --- Fusion methods based on a document's ranking position --- p.23 / Chapter 2.2.3 --- Fusion methods based on a document's URL title and snippets --- p.30 / Chapter 2.2.4 --- Fusion methods based on a document's entire content --- p.40 / Chapter 2.3 --- Comparison of the Fusion Methods --- p.42 / Chapter 2.4 --- Relevance Feedback --- p.46 / Chapter 3 --- Research Methodology --- p.48 / Chapter 3.1 --- Investigation of the features of the retrieved results from the search engines --- p.48 / Chapter 3.2 --- Types of relationships --- p.53 / Chapter 3.3 --- Order of Strength of the Relationships --- p.64 / Chapter 3.3.1 --- Derivation of the weight for each kind of relationship (criterion) --- p.68 / Chapter 3.4 --- Observation of the relationships between retrieved objects and the effects of these relationships on the relevance of objects --- p.69 / Chapter 3.4.1 --- Observation on the relationships existed in items that are irrelevant and relevant to the query --- p.68 / Chapter 3.5 --- Proposed re-ranking algorithms --- p.89 / Chapter 3.5.1 --- Original re-ranking algorithm (before modification) --- p.91 / Chapter 3.5.2 --- Modified re-ranking algorithm (after modification) --- p.95 / Chapter 4 --- Evaluation Methodology and Experimental Results --- p.101 / Chapter 4.1 --- Objective --- p.101 / Chapter 4.2 --- Experimental Design and Setup --- p.101 / Chapter 4.2.1 --- Preparation of data --- p.101 / Chapter 4.3 --- Evaluation Methodology --- p.104 / Chapter 4.3.1 --- Evaluation of the relevance of a document to the corresponding query --- p.104 / Chapter 4.3.2 --- Performance Measures of the Evaluation --- p.105 / Chapter 4.4 --- Experimental Results and Interpretation --- p.106 / Chapter 4.4.1 --- Precision --- p.107 / Chapter 4.4.2 --- Recall --- p.107 / Chapter 4.4.3 --- F-measure --- p.108 / Chapter 4.4.4 --- Overall evaluation results for the ten queries for each evaluation tool --- p.110 / Chapter 4.4.5 --- Discussion --- p.123 / Chapter 4.5 --- Degree of difference between the performance of systems --- p.124 / Chapter 4.5.1 --- Analysis using One-Way ANOVA --- p.124 / Chapter 4.5.2 --- Analysis using paired samples T-test --- p.126 / Chapter 5 --- Conclusion --- p.131 / Chapter 5.1 --- "Implications, Limitations, and Future Work" --- p.131 / Chapter 5.2 --- Conclusions --- p.133 / Bibliography --- p.134 / Chapter A --- Paired samples T-test for F-measures of systems retrieving all media's items --- p.140

Thesis (M.S.)--University of Florida, 2002. / Title from title page of source document. Includes vita. Includes bibliographical references.

Google Inc. struggles arduously on the digital battlefield in China’s Internet search engine market. In China, Baidu.com has been described as China’s Google for years and challenged Google’s expansion. This study provides an overview of the Internet service development in China, an illustration of the search engines’ profitability models, and an evaluation of Guge (Google China) and Baidu’s service designs. Overall, the research shows an attempt to understand the possible advantages and disadvantages when a multinational Internet service company enters China. Two notions emerge. First, standardization and adaptation may need to be nicely balanced for the subsidiary company in order to profit in China’s Internet market. Second, Google’s operation in China, Guge, stands strong on the service design end, especially in the area of “ease of use,” “informativeness,” and “fulfillment/reliability.” However, Guge’s major rival, Baidu, shows its advantage on a wider selection of online services. Therefore, in the long run, which company will win at the finishing line is still too early to tell / Google in China -- Google, Baidu and Guge -- Search engine's revenue model in China. / Department of Telecommunications

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2005. / Includes bibliographical references (leaves 102-119). Also available online.

Thesis (Ph. D.)--State University of New York at Binghamton, Dept. of Computer Science, 2007. / Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (p. 175-188).

Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (p. 177-189).

Thesis (M.A.)--Ball State University, 2009. / Title from PDF t.p. (viewed on Apr. 16, 2010). Includes bibliographical references (p. 54-63).

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

abstract: Many web search improvements have been developed since the advent of the modern search engine, but one underrepresented area is the application of specific customizations to search results for educational web sites. In order to address this issue and improve the relevance of search results in automated learning environments, this work has integrated context-aware search principles with applications of preference based re-ranking and query modifications. This research investigates several aspects of context-aware search principles, specifically context-sensitive and preference based re-ranking of results which take user inputs as to their preferred content, and combines this with search query modifications which automatically search for a variety of modified terms based on the given search query, integrating these results into the overall re-ranking for the context. The result of this work is a novel web search algorithm which could be applied to any online learning environment attempting to collect relevant resources for learning about a given topic. The algorithm has been evaluated through user studies comparing traditional search results to the context-aware results returned through the algorithm for a given topic. These studies explore how this integration of methods could provide improved relevance in the search results returned when compared against other modern search engines. / Dissertation/Thesis / Masters Thesis Computer Science 2014