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Über dieses Buch

This research volume focuses on analyzing the web user browsing behaviour and preferences in traditional web-based environments, social networks and web 2.0 applications, by using advanced techniques in data acquisition, data processing, pattern extraction and cognitive science for modeling the human actions.
The book is directed to graduate students, researchers/scientists and engineers interested in updating their knowledge with the recent trends in web user analysis, for developing the next generation of web-based systems and applications.



New Trends in Web User Behaviour Analysis

The analysis of human behaviour has been conducted within diverse disciplines, such as psychology, sociology, economics, linguistics, marketing and computer science. Hence, a broad theoretical framework is available, with a high potential for application into other areas, in particular to the analysis of web user browsing behaviour. The above mentioned disciplines use surveys and experimental sampling for testing and calibrating their theoretical models. With respect to web user browsing behaviour, the major source of data is the web logs, which store every visitor’s action on a web site. Such files could contain millions of registers, depending on the web site traffic, and represents a major data source about human behaviour. This chapter surveys the new trends in analysing web user behaviour and revises some novel approaches, such as those based on the neurophysiological theory of decision making, for describing what web users are looking for in a web site.
Pablo E. Román, Juan D. Velásquez, Vasile Palade, Lakhmi C. Jain

Web Usage Data Pre-processing

End users leave traces of behavior all over the Web all times. From the explicit or implicit feedback of a multimedia document or a comment in an online social network, to a simple click in a relevant link in a search engine result, the information that we as users pour into the Web defines its actual representation, which is independent for each user. Our usage can be represented by different sources of data, for which different collection strategies must be considered, as well as the merging and cleaning techniques for Web usage data. Once the data is properly preprocessed, the identification of an individual user within the Web can be a complex task. Understanding the whole life of a user within a session in a Web site and the path that was pursued involves advanced data modeling and a set of assumptions which are modified every day, as new ways to interact with the online content are created. The objective is to understand the behaviour and preferences of a web user, also when several privacy issues are involved, which, as of today, are not clear how to be properly addressed. In this chapter, all previous topics regarding the processing of Web usage data are extensively discussed.
Gaston L’Huillier, Juan D. Velásquez

Cognitive Science forWeb Usage Analysis

Web usage mining is the process of extracting patterns from web user’s preferences and browsing behavior. Furthermore, the web user behavior refers to the user’s activities in a web site. Cognitive science is a multi-disciplinary approach used for the understanding of human behavior, whose aims is to develop models of information processing in the real brain. Therefore, cognitive sciences can have direct application to web usage mining. In this chapter, some state-of-the-art psychology theories are presented in the context of web usage analysis. In spite of the complexity of neural processes in the brain, stochastic models based on diffusion can be used to explain a decision-making process, and this has been experimentally tested. Diffusion models and theirs application to describe web usage are reviewed in this chapter. An example of application of cognitive science to web usage mining is also presented.
Pablo E. Román, Juan D. Velásquez

Web Usage Mining: Discovering Usage Patterns for Web Applications

The heterogeneous nature of the Web combined with the rapid diffusion of Web-based applications have made Web browsing an intricate activity for users. This has given rise to an urgent need for developing systems capable to assist and guide users during their navigational activity in the Web. Web Usage Mining (WUM) refers to the application of Data Mining techniques for the automatic discovery of meaningful usage patterns characterizing the browsing behavior of users, starting from access data collected from interactions of users with sites. The discovered patterns may be conveniently exploited in order to implement functionalities offering useful assistance to users. This chapter is mainly intended to provide an overview of the different stages involved in a general WUM process. As an example, a WUM approach is presented which is based on the use of fuzzy clustering to discovery user categories starting from usage patterns.
Giovanna Castellano, Anna M. Fanelli, Maria A. Torsello

Web Opinion Mining and Sentimental Analysis

Web Opinion Mining (WOM) is a new concept in Web Intelligence. It embraces the problem of extracting, analyzing and aggregating web data about opinions. Studying users’ opinions is relevant because through them it is possible to determine how people feel about a product or service and know how it was received by the market. In this chapter, we show an overview about what Opinion Mining is and give some approaches about how to do it. Also, we distinguish and discuss four resources from where opinions can be extracted from, analyzing in each case the main issues that could alter the mining process. One last interesting topic related to WOM and discussed in this chapter is the summarization and visualization of the WOM results.We consider these techniques to be important because they offer a real chance to understand and find a real value for a huge set of heterogeneous opinions collected. Finally, having given enough conceptual background, a practical example is presented using Twitter as a platform for Web Opinion Mining. Results show how an opinion is spread through the network and describes how users influence each other.
Edison Marrese Taylor, Cristián Rodríguez O., Juan D. Velásquez, Goldina Ghosh, Soumya Banerjee

Web Usage Based Adaptive Systems

The Internet is becoming an important tool for the realization of day-to-day activities, which leads to a new level of interaction between users and software systems. This new scenario presents endless opportunities as well as enormous challenges. In order to tackle these, user-adaptive software systems have been recently used. These technologies aim to allow computer systems to dynamically modify their content, structure and presentation for better delivery of the available resources, while considering the user’s interest and behavior, and most recently, mobile environments. This chapter overviews the newest technologies in the area of user-adaptive software systems applied to Web environments and proposes a set of directions for the future development of Web Usage Based Adaptive Systems in the new Internet environments.
Pablo Loyola Heufemann, Jorge Gaete Villegas, In-Young Ko

Recommender Systems: Sources of Knowledge and Evaluation Metrics

Recommender or Recommendation Systems (RS) aim to help users dealing with information overload: finding relevant items in a vast space of resources. Research on RS has been active since the development of the first recommender system in the early 1990s, Tapestry, and some articles and books that survey algorithms and application domains have been published recently. However, these surveys have not extensively covered the different types of information used in RS (sources of knowledge), and only a few of them have reviewed the different ways to assess the quality and performance of RS. In order to bridge this gap, in this chapter we present a classification of recommender systems, and then we focus on presenting the main sources of knowledge and evaluation metrics that have been described in the research literature.
Denis Parra, Shaghayegh Sahebi


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