Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, 2004. / Includes bibliographical references.

Inefficiencies associated with online information search are becoming increasingly prevalent in digital environments due to a surge in Consumer Generated Content (CGC). Despite growing scholarly interest in investigating users' information search behavior in CGC environments, there is a paucity of studies that explores the phenomenon from a theory-guided angle. Drawing on Information Foraging Theory (IFT), we re-conceptualize online information search as a form of adaptive user behavior in response to system design constraints. Through this theoretical lens, we advance separate taxonomies for online information search tactics and strategies, both of which constitute essential building blocks of the search process. Furthermore, we construct a research framework that bridges the gap between online information search tactics and strategies by articulating how technology-enabled search tactics contribute to the fulfillment of strategic search goals. We validate our research framework via an online experiment by recruiting participants from Amazon Mechanical Turk (AMT). Participants were tasked to perform searches on custom-developed online review websites, which were modeled after a popular online review website and populated with real restaurant review data. Empirical findings reveal that the provision of different search features indeed engenders distinct search tactics, thereby allowing users varying levels of search determination control and search manipulation control. In turn, both types of search controls affects users' result anticipation and search costs, which when combined, determine the efficiency of goal-oriented search strategy and the utility of exploratory search strategy. This study provides valuable insights that can guide future research and practice.

No description available.

University of Macau / Faculty of Social Sciences. / Department of Sociology

This thesis presents methods and a system for video search over the internet or the intranet. The objective is to design a real time and automated video clustering and search system that provides users of the search engine the most relevant videos available that are responsive to a query at a particular moment in time, and supplementary information that may also be useful. The thesis highlights methods to mitigate the effect of the semantic gap faced by current content based video search approaches. A context-sensitive video ranking scheme is used, wherein the context is generated in an automated manner.

Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, Thomas J. Watson School of Engineering and Applied Science, 2007. / Includes bibliographical references.

Thesis (Ph.D.)--Flinders University, School of Informatics and Engineering. / Typescript bound. Includes bibliographical references: (leaves 311-329) Also available online.

Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 88-93). Also available on the World Wide Web.

Thesis--Auburn University, 2009. / Abstract. Vita. Includes bibliographical references (p. 47).

Thesis (M.C.S.) - Carleton University, 2007. / Includes bibliographical references (p. 131-137). Also available in electronic format on the Internet.