Mining the web to create minority language corpora
Proceedings of the tenth international conference on Information and knowledge management
Some Formal Analysis of Rocchio's Similarity-Based Relevance Feedback Algorithm
Information Retrieval
A Taxonomy of Recommender Agents on theInternet
Artificial Intelligence Review
Online Learning for Web Query Generation: Finding Documents Matching a Minority Concept on the Web
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Some Formal Analysis of Roccio's Similarity-Based Relvance Feedback Algorithm
ISAAC '00 Proceedings of the 11th International Conference on Algorithms and Computation
Multiplicative Adaptive Algorithms for User Preference Retrieval
COCOON '01 Proceedings of the 7th Annual International Conference on Computing and Combinatorics
Building Minority Language Corpora by Learning to Generate Web Search Queries
Knowledge and Information Systems
A study of meta-search agent based on tags and ontological approach for improving web searches
KES-AMSTA'08 Proceedings of the 2nd KES International conference on Agent and multi-agent systems: technologies and applications
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In this paper, we investigate the applicability of on-line learning algorithms to the real-world problem of web search. Consider that web documents are indexed using n Boolean features. We first present a practically efficient on-line learning algorithm TW2 to search for web documents represented by a disjunction of at most k relevant features. We then design and implement WebSail, a real-time adaptive web search learner, with TW2 as its learning component. Web-Sail learns from the user's relevance feedback in real-time and helps the user to search for the desired web documents. The architecture and performance of WebSail are also discussed.