iJADE IWShopper: A New Age of Intelligent Mobile Web Shopping System Based on Fuzzy-Neuro Agent Technology

Lee, Raymond

doi:10.1007/3-540-45490-x_51

Cited by 8 publications

(3 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [31], fuzzy association rules understandable to humans are learnt from a database containing both quantitative and categorical attributes by using a neuro-fuzzy approach like the one proposed by Nauck [75]. Lee [55] uses a NF system for recommendation in an e-commerce site. Stathacopoulou et al [99] and Magoulas et al [60] use NF to implement a classification or recommendation system with the purpose of adapting the contents of a Web-course according to the model of the student.…”

Section: Hybrid CI Techniques In Web Personalizationmentioning

confidence: 99%

Computational Intelligence techniques for Web personalization

Castellano¹,

Fanelli²,

Torsello³

2008

Web Intelligence and Agent Systems: An International Journal

View full text Add to dashboard Cite

Computational Intelligence (CI) paradigms reveal to be potential tools to face under the Web uncertainty. In particular, CI techniques may be properly exploited to handle Web usage data and develop Web-based applications tailored on users preferences. The main rationale behind this success is the synergy resulting from CI components, such as fuzzy logic, neural networks and genetic algorithms. In fact, rather than being competitive, each of these computing paradigms provides complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. This paper focuses on the major Computational Intelligent combinations applied in the context of Web personalization, by providing different examples of intelligent systems which have been designed to provide Web users with the information they search, without expecting them to ask for it explicitly. In particular, this paper emphasizes the suitability of hybrid schemes deriving from the profitable combination of different CI methodologies for the development of effective Web personalization systems.

show abstract

Section: Hybrid CI Techniques In Web Personalizationmentioning

confidence: 99%

Computational Intelligence techniques for Web personalization

Castellano¹,

Fanelli²,

Torsello³

2008

Web Intelligence and Agent Systems: An International Journal

View full text Add to dashboard Cite

show abstract

“…One of the most important NFS algorithms is Adaptive‐Network‐based Fuzzy Inference Systems (ANFIS; Jang, 1993), which has been used in a wide range of applications (Bonisone, Badami, & Chiang, 1995). The combination of NN and fuzzy sets offers a powerful method to model human behavior, which allows NFS to be used for a variety of user modeling tasks (Drigas, Kouremenos, S., Vrettos, Vrettaros, & Kouremenos, D., 2004; Lee, 2002; Magoulas et al, 2001; Stathacopoulou, Grigoriadou, & Magoulas, 2003).…”

Section: Cognitive Automatic Classification Systemmentioning

confidence: 99%

Automatic cognitive style identification of digital library users for personalization

Frı́as-Martı́nez

Chen

Liu

2006

J. Am. Soc. Inf. Sci.

View full text Add to dashboard Cite

Digital libraries have become one of the most important Web services for information seeking. One of their main drawbacks is their global approach: In general, there is just one interface for all users. One of the key elements in improving user satisfaction in digital libraries is personalization. When considering personalizing factors, cognitive styles have been proved to be one of the relevant parameters that affect information seeking. This justifies the introduction of cognitive style as one of the parameters of a Web personalized service. Nevertheless, this approach has one major drawback: Each user has to run a time‐consuming test that determines his or her cognitive style. In this article, we present a study of how different classification systems can be used to automatically identify the cognitive style of a user using the set of interactions with a digital library. These classification systems can be used to automatically personalize, from a cognitive‐style point of view, the interaction of the digital library and each of its users.

show abstract

“…The combination of NN and fuzzy sets offers a powerful method to model human behavior which allows NFS to be used for a variety of tasks. [21] and [35] use a NFS for Recommendation in an e-commerce site and for an on-line course respectively. [22] uses NFS to implement multi-attribute decision making with the purpose of planning the contents of a web-course according to the knowledge level of the student.…”

Section: Neuro-fuzzy Systemsmentioning

confidence: 99%

Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia

Frı́as-Martı́nez

Magoulas

Chen

et al. 2004

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. 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.

show abstract

iJADE IWShopper: A New Age of Intelligent Mobile Web Shopping System Based on Fuzzy-Neuro Agent Technology

Cited by 8 publications

References 3 publications

Computational Intelligence techniques for Web personalization

Computational Intelligence techniques for Web personalization

Automatic cognitive style identification of digital library users for personalization

Recent Soft Computing Approaches to User Modeling in Adaptive Hypermedia

Contact Info

Product

Resources

About