Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Modelling both the Context and the User
Personal and Ubiquitous Computing
Very Large Two-Level SOM for the Browsing of Newsgroups
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Web page clustering using a self-organizing map of user navigation patterns
Decision Support Systems - Special issue: Web data mining
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
Intelligent web traffic mining and analysis
Journal of Network and Computer Applications - Special issue on computational intelligence on the internet
Proceedings of the 15th international conference on World Wide Web
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Identifying "best bet" web search results by mining past user behavior
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Neural Networks with Java
Introduction to Neural Networks with Java
Behavior based web page evaluation
Proceedings of the 16th international conference on World Wide Web
Toward a multidisciplinary model of context to support context-aware computing
Human-Computer Interaction
Hi-index | 0.00 |
In the context of a highly volatile web of uneven quality, the identification of content deemed valuable by end users is of paramount importance. Where page content undergoes rapid change, this issue is particularly challenging. Web browsing activity represents a unique source of context by which the value of web pages can be determined via an assessment of individual user interactions, such as scrolling, clicking, saving and so forth. Over time, this data set forms a pattern of activity which can be mined for meaning. In this paper we present an approach to web content, based on Kohonen mapping, used to generate a topological model of users' behaviour over web-pages. Each web-document can thus be represented as a semantic map built by adopting unsupervised techniques where similar users' behaviour are mapped close together, with identification of information stability emerging as a by product of the identification of similarity in user activity over content. In this model, the more similar the outputs of the map for each user who has endorsed a web-page, the more the web site is considered current or in context with changing information. We illustrate the potential application of this approach to our ongoing work in social search.