Relevance and Reinforcement in Interactive Browsing

  title={Relevance and Reinforcement in Interactive Browsing},
  author={Anton Leuski},
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… CONTINUE READING
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