Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Automatically organizing bookmarks per contents
Proceedings of the fifth international World Wide Web conference on Computer networks and ISDN systems
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
WebMate: a personal agent for browsing and searching
AGENTS '98 Proceedings of the second international conference on Autonomous agents
WebACE: a Web agent for document categorization and exploration
AGENTS '98 Proceedings of the second international conference on Autonomous agents
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Letizia: an agent that assists web browsing
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Word weighting based on user's browsing history
UM'03 Proceedings of the 9th international conference on User modeling
Extending a web browser with client-side mining
APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications
Profiling multiple domains of user interests and using them for personalized web support
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
| Hi-index | 0.00 |
Personalised context sensitivity is the Holy Grail of web information retrieval. As a first step towards this goal, we present the Web Personae personalised search and browsing system. We use well-known information retrieval techniques to develop and track user models. Web Personae differ from previous approaches in that we model users with multiple profiles, each corresponding to a distinct topic or domain. Such functionality is essential in heterogeneous environments such as the Web. We introduce Web Personae, describe an algorithm for learning such models from browsing data, and discuss applications and evaluation methods.