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ERIC Number: ED552672
Record Type: Non-Journal
Publication Date: 2012
Pages: 177
Abstractor: As Provided
Reference Count: N/A
ISBN: 978-1-2679-7666-6
Modeling Rich Interactions for Web Search Intent Inference, Ranking and Evaluation
Guo, Qi
ProQuest LLC, Ph.D. Dissertation, Emory University
Billions of people interact with Web search engines daily and their interactions provide valuable clues about their interests and preferences. While modeling search behavior, such as queries and clicks on results, has been found to be effective for various Web search applications, the effectiveness of the existing approaches are limited by ignoring what the searcher sees ("examination") and does ("context") before clicking a result. This thesis aims to address these limitations by modeling and interpreting a wider range of searcher interactions, including mouse cursor movement and scrolling behavior (or pinching, zooming and sliding with a touch screen), that could be served as a proxy of searcher examination, contextualized in a search session. The thesis focuses on improving three fundamental and interrelated areas of Web search, namely, "intent inference," "ranking" and "evaluation." To improve the first area, the thesis developed techniques to infer the immediate search goals in a search session, along multiple dimensions, including top-level general intent (e.g., navigational vs. informational), commercial intent (e.g., research vs. purchase) and advertising receptiveness (i.e., interest in search ads). To improve the second area, the thesis developed the "Post-Click Behavior" ("PCB") relevance prediction model for estimating the "intrinsic" document relevance from the examination and interaction patterns on the viewed result documents. To improve the third area, the thesis developed techniques for predicting search success, which include a principled framework to study Web search success, and fine-grained interaction models that improve prediction accuracy for both desktop and mobile settings. As demonstrated with extensive empirical evaluation, the developed techniques outperform the state-of-the-art methods that only use query, click and time signals, enabling more intelligent Web search systems. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page:]
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Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A