Relevance and Reinforcement in Interactive Browsing

We consider the problem of browsing the top ranked portion of the documents returned by an information retrieval system. We describe an interactive relevance feedback agent that analyzes the inter-document similarities and can help the user to locate the interesting information quickly. We show how such an agent can be designed and improved by using neural networks and reinforcement learning. We demonstrate that its performance significantly exceeds the performance of the traditional relevance feedback approach. Categories and Subject Descriptors H.3.3 [Information storage and retrieval]: Information Search and Retrieval—Relevance feedback, Selection process; H.5.m [Information Interfaces and Presentation]: Miscellaneous; I.2.6 [Artificial Intelligence]: Learning—Connectionism and neural nets General Terms Experimentation, performance, algorithms

• Type: Conference
• Year: 2000
• Where: CIKM
• Authors: Anton Leuski
• Keywords: CIKM 2000, Information Management, Relevance Feedback, Relevance Feedback Agent, Relevance Feedback Approach