The Role of Human Factors in Web Personalization Environments

Abstract

The explosive growth in the size and use of the World Wide Web as a communication medium as well as the new developments in ICT allowed service providers to meet these challenges, developing new ways of interactions through a variety of channels enabling users to become accustomed to new means of service consumption in an “anytime, anywhere and anyhow” manner. However, the nature of most information structures is static and complicated, and users often lose sight of the goal of their inquiry, look for stimulating rather than informative material, or even use the navigational features unwisely. Hence, researchers and practitioners studied adaptivity and personalization to address the comprehension and orientation difficulties presented in such systems, to alleviate such navigational difficulties and satisfy the heterogeneous needs of the users, allowing at the same time Web applications of this nature to survive. There are many approaches to address these issues of personalization but usually, each one is focused upon a specific area, that is, whether this is profile creation, machine learning and pattern matching, data and Web mining or personalized navigation. Some noteworthy, mostly commercial, applications in the area of Web personalization that collect information with various techniques and further adapts the services provided, are among others the Broadvision’s One-To-One, Microsoft’s Firefly Passport, the Macromedia’s LikeMinds Preference Server, the Apple’s WebObjects, and so forth. Other, more research-oriented systems, include ARCHIMIDES (Bogonikolos et al., 1999), Proteus (Anderson et al., 2001), WBI (Magglio & Barret, 2001), BASAR (Thomas & Fischer, 1997), and mPERSONA (Panayiotou & Samaras, 2004). Significant implementations have also been developed in the area of adaptive hypermedia, with regards to the provision of adapted educational content to students using various adaptive hypermedia techniques. Such systems are, among others, INSPIRE (Papanikolaou, Grigoriadou, Kornilakis, & Magoulas, 2003), ELM-ART (Weber & Specht, 1997), AHA! (De Bra & Calvi, 1998), Interbook (Brusilovsky, Eklund, & Schwartz, 1998), and so forth.

Key Terms in this Chapter

Cognition: A human-like processing of information, applying knowledge and changing preferences. Cognition or cognitive processes can be natural and artificial, conscious and not conscious; therefore, they are analyzed from different perspectives and in different contexts, in anesthesia, neurology, psychology, philosophy, systemics and computer science.

Emotional Intelligence: It describes an ability, capacity, or skill to perceive, assess, and manage the emotions of one’s self, of others, and of groups.

User Modeling: User modeling is a subarea of human-computer interaction, in which the researcher/designer develops cognitive models of human users, including modeling of their skills and declarative knowledge. User models can predict human error and learning time.

Visual Processing: It is the sequence of steps that information takes as it flows from visual sensors to cognitive processing.

Cognitive Styles: They are consistent individual differences in preferred ways of organizing and processing information and experience

User Perceptual Preference Characteristics: User Perceptual Preference Characteristics are all the critical factors that influence the visual, mental and emotional processes liable of manipulating the newly information received and building upon prior knowledge, that is, different for each user or user group. These characteristics determine the visual attention, cognitive and emotional processing taking place throughout the whole process of accepting an object of perception (stimulus) until the comprehensive response to it.