Due to the information explosion on the Internet, effective information search techniques are required to retrieve the desired information from the Web. Based on much analysis on users' search intention and the variant forms of Web content, we find that both the query and the indexed web content are often associated with various context information, which can provide much essential information to indicate the ranking relevance in Web search. This dissertation seeks to develop new search algorithms and techniques by taking advantage of rich context information to improve search quality and consists of two major parts. In the first part, we study the context of the query in terms of various ranking objectives of different queries. In order to improve the ranking relevance, we propose to incorporate such query context information into the ranking model. Two general approaches will be introduced in the following of this dissertation. The first one proposes to incorporate query difference into ranking by introducing query-dependent loss functions, by optimizing which we can obtain better ranking model. Then, we investigate another approach which applies a divide-and-conquer framework for ranking specialization. The second part of this dissertation investigates how to extract the context of specific Web content and explore them to build more effective search system. This study is based on the new emerging social media content. Unlike traditional Web content, social media content is inherently associated with much new context information, including content semantics and quality, user reputation, and user interactions, all of which provide useful information for acquiring knowledge from social media. In this dissertation, we seek to develop algorithms and techniques for effective knowledge acquisition from collaborative social media environments by using the dynamic context information. We first propose a new general framework for searching social media content, which integrates both the content features and the user interactions. Then, a semi-supervised framework is proposed to explicitly compute content quality and user reputation, which are incorporated into the search framework to improve the search quality. Furthermore, this dissertation also investigates techniques for extracting the structured semantics of social media content as new context information, which is essential for content retrieval and organization.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2004. / Includes bibliographical references (p. 37-38).

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

This exploratory research investigates the factors that might influence a specific group of users’ mental models of a Web search engine, Google, as measured in the dimension of completeness. A modified mental model completeness scale (MMCS) was developed based on Borgman’s, Dimitroff s, and Saxon’s models, encompassing the perception of (1) the nature of the Web search engine, (2) searching features of the Web search engine, and (3) the interaction between the searcher and the Web search engine. With this scale, a participant’s mental model completeness level was determined by how many components of the first two parts of the scale were described and which level of interaction between the participant and Google was revealed during the searches. The choice of the factors was based on the previous studies on individual differences among information seekers, including user’s search experience, cognitive style, learning style, technical aptitudes, training received, discipline, and gender. Sixteen Ph.D. students whose first language is English participated in the research. Individual semi-structured interviews were conducted to determine the students’ mental model completeness level (MMCL) as well as their search experience, training received, discipline and gender. Direct observation technique was employed to observe students’ actual interactions with Google. Standard tests were administered to determine the students’ cognitive styles, learning styles and technical aptitudes. / Cette recherche préliminaire examine les facteurs qui peuvent influencer les modèles mentaux d’un groupe spécifique d’utilisateurs d’un moteur de recherche sur le Web: Google, mesurés selon l’étendue de leur réussite.Une échelle de cette réussite en suivant un modèle mental a été constituée en adaptant les modèles présentés par Borgman, Dimitroff et Saxon, incluant la perception (1) de la nature du moteur de recherche sur le Web, (2) des caractéristiques de la recherche propres à ce moteur, (3) de l’interaction entre le chercheur et le moteur de recherche. A l’aide de cette échelle, le niveau de réussite par un sujet donné utilisant un modèle mental a été déterminé en fonction du nombre de composantes des deux premières parties de l’échelle décrites et du niveau d’interaction entre le sujet et le moteur Google, tel que révélé par ses recherches. Le choix des facteurs a été fondé sur des études précédentes portant sur les différences individuelles entre les chercheurs d’information, comprenant le degré d’expérience d’une telle recherche par l’utilisateur, son style cognitif, son style d’apprentissage, ses aptitudes techniques, la formation reçue, la discipline et le sexe. Seize étudiants en doctorat ayant l’anglais comme première langue ont participé à cette étude. Des entretiens individuels semi-dirigés ont permis de déterminer le niveau de réussite des étudiants suivant leur modèle mental, ainsi que leur expérience de la recherche, la formation reçue, la discipline et le sexe. Une observation technique directe a été utilisée pour observer l’interaction réelle des étudiants avec Google. Des tests standardisés ont été administrés pour déterminer le style cognitif des étudiants, leur style d’apprentissage et leurs aptitudes techniques. fr

Proefschrift Universiteit van Amsterdam. / Met lit.opg. en samenvatting in het Nederlands.

Thesis(M.S.)--Auburn University, 2005. / Abstract. Vita. Includes bibliographic references.

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.

Title from title page of PDF (University of Missouri--St. Louis, viewed February 17, 2010). Includes bibliographical references (p. 36-38).