Three dimensional visualization of the World Wide Web

Although large-scale public hypermedia structures such as the World Wide Web are popularly referred to as "cyberspace", the extent to which they constitute a space in the everyday sense of the word is questionable. This paper reviews recent work in the area of three dimensional (3D) visualization of the Web that has attempted to depict it in the form of a recognizable space; in other words, as a navigable landscape that may be visibly populated by its users. Our review begins by introducing a range of visualizations that address different aspects of using the Web. These include visualizations of Web structure, especially of links, that act as 3D maps; browsing history; searches; evolution of the Web; and the presence and activities of multiple users. We then summarize the different techniques that are employed by these visualizations. We conclude with a discussion of key challenges for the future.

'An Artificial Immune System as a Recommender System for Web Sites'

Artificial Immune Systems have been used successfully to build recommender systems for film databases. In this research, an attempt is made to extend this idea to web site recommendation. A collection of more than 1000 individuals' web profiles (alternatively called preferences / favourites / bookmarks file) will be used. URLs will be classified using the DMOZ (Directory Mozilla) database of the Open Directory Project as our ontology. This will then be used as the data for the Artificial Immune Systems rather than the actual addresses. The first attempt will involve using a simple classification code number coupled with the number of pages within that classification code. However, this implementation does not make use of the hierarchical tree-like structure of DMOZ. Consideration will then be given to the construction of a similarity measure for web profiles that makes use of this hierarchical information to build a better-informed Artificial Immune System.

'An Artificial Immune System as a Recommender System for Web Sites'

Artificial Immune Systems have been used successfully to build recommender systems for film databases. In this research, an attempt is made to extend this idea to web site recommendation. A collection of more than 1000 individuals' web profiles (alternatively called preferences / favourites / bookmarks file) will be used. URLs will be classified using the DMOZ (Directory Mozilla) database of the Open Directory Project as our ontology. This will then be used as the data for the Artificial Immune Systems rather than the actual addresses. The first attempt will involve using a simple classification code number coupled with the number of pages within that classification code. However, this implementation does not make use of the hierarchical tree-like structure of DMOZ. Consideration will then be given to the construction of a similarity measure for web profiles that makes use of this hierarchical information to build a better-informed Artificial Immune System.