Enrique Frias-Martinez

Modeling human behavior in user-adaptive systems: Recent advances using soft computing techniques

Adaptive Hypermedia systems are becoming more important in our everyday activities and users are expecting more intelligent services from them. The key element of a generic adaptive hypermedia system is the user model. Traditional machine learning techniques used to create user models are usually too rigid to capture the inherent uncertainty of human behavior. In this context, soft computing techniques can be used to handle and process human uncertainty and to simulate human decision-making. This paper examines how soft computing techniques, including fuzzy logic, neural networks, genetic algorithms, fuzzy clustering and neuro-fuzzy systems, have been used, alone or in combination with other machine learning techniques, for user modeling from 1999 to 2004. For each technique, its main applications, limitations and future directions for user modeling are presented. The paper also presents guidelines that show which soft computing techniques should be used according to the task implemented by the application.

Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the application

Automated user modeling for personalized digital libraries

Digital libraries (DLs) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from DLs. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in DLs has been user driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct DLs that satisfy a users necessity for information: Adaptive DLs, libraries that automatically learn user preferences and goals and personalize their interaction using this information.

Evaluation of a personalized digital library based on cognitive styles: Adaptivity vs. adaptability

Personalization can be addressed by adaptability and adaptivity, which have different advantages and disadvantages. This study investigates how digital library (DL) users react to these two techniques. More specifically, we develop a personalized DL to suit the needs of different cognitive styles based on the findings of our previous work [Frias-Martinez, E., Chen, S. Y., & Liu, X. (2008) Investigation of behavior and perception of digital library users: A cognitive style perspective. International Journal of Information Management]. The personalized DL includes two versions: adaptive version and adaptable version. The results showed that users not only performed better in the adaptive version, but also they perceived more positively to the adaptive version. In addition, cognitive styles have great effects on users responses to adaptability and adaptivity. These results provide guidance for designers to select suitable techniques to develop personalized DLs.

The role of human factors in stereotyping behavior and perception of digital library users: a robust clustering approach

To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception.

Investigation of behavior and perception of digital library users: A cognitive style perspective

Cognitive style is an influential factor in users information seeking. The study presented in this paper examines how users cognitive styles affect their behavior and perception in digital libraries. Fifty participants took part in this study. Two dimensions of cognitive styles were considered: (a) Field Dependence/Independence; (2) Verbalizer/Imager. The results showed that Intermediate users and Verbalizers have not only more positive perception, but they also complete the tasks in effective ways. Implications for the design of personalized digital libraries are also discussed.

Understanding web site redesigns in small- and medium-sized enterprises (SMEs): a U.K.-based study on the applicability of e-commerce Stage Models

Despite the efforts of governments and the various support programmes, achievement of advanced stages of e-commerce by small- and medium-sized enterprises (SMEs) is still very low. There have been some attempts to study the dynamic nature of websites, but there is still little research evidence to explain why and how SMEs evolve their web presence. This paper aims to develop a comprehensive classification of drivers for web site redesign based on interviews with various members of staff from SMEs in the U.K. that have recently redesigned their web sites. A sequential mixed-methodological analysis, involving the use of qualitative and quantitative data analysis, was used to develop the classification. This enabled the development of a framework that classified seven main categories of drivers for web site redesign. The drivers identified were: changing business requirements, evolving internet strategies, addressing user needs, maintenance, changing technology, pressure from peers/competitors, and the influence of developers. However, only the first four were found to be significant in the study. The categorisation and the findings suggest a number of key determinants not explicitly addressed by other work. In addition, the findings provide little support for the staged approach to e-commerce progression as few companies reported the implementation of sophisticated internet technology features as a main reason for their web site redesigns. The contributions of this paper are firstly, to provide an instrument to the academic and practitioner communities interested in the topic of web site evolution. Secondly, the categorisation of drivers for redesign and the individual reasons found in this study are expected to provide assistance to SME managers to justify, plan and strategise internet investments realistically and effectively.

Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. One of the difficulties that user modeling faces is the necessity of capturing the imprecise nature of human behavior. Soft Computing has the ability to handle and process uncertainty which makes it possible to model and simulate human decision-making. This paper surveys different soft computing techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques should be used according to the task implemented by the application.

Automatic Generation of Cognitive Theories using Genetic Programming

Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories.