Analyzing and modeling web user behavior is important not just for providers of information on the web, but also to researchers in the social sciences and others who study or write about web-related economic and cultural trends. Information generators, user experience designers, service providers, advertisers, business data analysts, search algorithm designers, and political and news organizations are only some of numerous interested parties. Techniques for detecting trends and understanding user behavior are therefore quite important, representing an active area of research in computer science, information technology, and the social sciences. This short volume is a focused collection of papers on recent research in this area, following a previous volume in the same area [1]. The papers focus on analyzing and modeling user behavior and providing updates on recent research on web intelligence.

An introductory chapter summarizes trends in behavior analysis for web users, followed by six chapters by different researchers addressing topics such as preprocessing techniques, cognitive models of users, usage pattern discovery, sentiment analysis, and adaptive and recommender systems. Each chapter offers an overview of the basic issues and short but informative descriptions of sources, summaries of models, or conceptual constructs for analysis or understanding, as well as descriptions of processes, techniques, and metrics; some also include summaries of results or applications. Chapter 2 addresses techniques for detecting the behavior and preferences of individual users. Chapter 3 describes cognitive models. Chapter 4, on usage mining, describes the preliminary problems of data collection and preprocessing, followed by an overview of conceptual constructs for describing usage knowledge (associations, sequential patterns, clustering, and classification), a discussion of metrics and techniques, and results for the discovery of user categories. Chapter 5, on web opinion mining and sentiment analysis, sketches the issues and sources of opinion information. Much of this chapter deals with an experiment on opinion mining with Twitter content. Chapter 6 describes models and techniques for content and presentation adaptation. Chapter 7 explores sources, techniques, and metrics for recommender systems.

Taken together, this volume presents a progression of ideas, starting with data gathering, extending through behavioral characterization and user modeling, and ending with applications. Extensive references to the research literature accompany each chapter, and a rather brief subject index concludes the volume.

This book is suitable for researchers and graduate students looking for a survey of current trends in the modeling of web user behavior from the perspective of web data analysis or cognitive science.