Modeling User Behavior And Attention In Search
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Modeling User Behavior and Attention in Search
In Web search, query and click log data are easy to collect but they fail to capture user behaviors that do not lead to clicks. As search engines reach the limits inherent in click data and are hungry for more data in a competitive environment, mining cursor movements, hovering, and scrolling becomes important. This dissertation investigates how remotely collecting rich user interaction data in the form of mouse cursor activity can help researchers understand fundamental human behavior and improve the design of search engines. Specifically, mining cursor activity can improve upon state-of-the-art methods for scoring and ranking search results, and estimating where users are looking without eye-tracking. Descriptive analyses of cursor movements show how users move their cursor when they search to provide signals of relevance and explain reasons for abandoning a search. User models can be used to infer visual attention on the page to identify what content users are looking at, as well as compute the relevance and attractiveness of search results to the user. This implicit feedback given to the search engine can then inform the layout and content presented on the pages, or improve the ranking of search results. This dissertation will demonstrate the following thesis: users' mouse cursor interactions can be collected efficiently on the Web, used to understand users' search behaviors, and can be useful in the design of Web search engines.
Library and Information Science Research in Asia-Oceania: Theory and Practice
Historically, the major Library and Information Science (LIS) research-producing centers of the world have largely been the universities and information institutions of North America, the United Kingdom, and Europe. This is changing with the growth of Asian economies, universities, and information industries. Library and Information Science Research in Asia-Oceania: Theory and Practice presents evolving and emerging research and development in the field of library and information science (LIS) in diverse countries in Asia-Oceania as the region continues to develop. This book is intended as a useful resource for LIS researchers, scholars, students, professionals, and practitioners, and is an appropriate text for courses in LIS. In addition, anyone interested in understanding the LIS field in the region will find this book a fascinating and enlightening read.
A Behavioral Economics Approach to Interactive Information Retrieval
This book brings together the insights from three different areas, Information Seeking and Retrieval, Cognitive Psychology, and Behavioral Economics, and shows how this new interdisciplinary approach can advance our knowledge about users interacting with diverse search systems, especially their seemingly irrational decisions and anomalies that could not be predicted by most normative models. The first part “Foundation” of this book introduces the general notions and fundamentals of this new approach, as well as the main concepts, terminology and theories. The second part “Beyond Rational Agents” describes the systematic biases and cognitive limits confirmed by behavioral experiments of varying types and explains in detail how they contradict the assumptions and predictions of formal models in information retrieval (IR). The third part “Toward A Behavioral Economics Approach” first synthesizes the findings from existing preliminary research on bounded rationality and behavioral economics modeling in information seeking, retrieval, and recommender system communities. Then, it discusses the implications, open questions and methodological challenges of applying the behavioral economics framework to different sub-areas of IR research and practices, such as modeling users and search sessions, developing unbiased learning to rank and adaptive recommendations algorithms, implementing bias-aware intelligent task support, as well as extending the conceptualization and evaluation on IR fairness, accountability, transparency and ethics (FATE) with the knowledge regarding both human biases and algorithmic biases. This book introduces a behavioral economics framework to IR scientists seeking a new perspective on both fundamental and new emerging problems of IR as well as the development and evaluation of bias-aware intelligent information systems. It is especially intended for researchers working on IR and human-information interaction who want to learn about the potential offered by behavioral economics in their own research areas.