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Long-Term Learning for Web Search Engines

Abstract. This paper considers how web search engines can learn from the successful searches recorded in their user logs. Document Transformation is a feasible approach that uses these logs to improve document representations. Existing test collections do not allow an adequate investigation of Document Transformation, but we show how a rigorous evaluation of this method can be carried out using the referer logs kept by web servers.

We also describe a new strategy for Document Transformation that is suitable for long-term incremental learning. Our experiments show that Document Transformation improves retrieval performance over a medium sized collection of webpages. Commercial search engines may be able to achieve similar improvements by incorporating this approach.

By Charles Kemp and Kotagiri Ramamohanarao


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The Internet in Real Time

Ever wonder how much is going on at once on the Internet? It can be tough to wrap your mind around it, but we’ve put together a nice visual that’ll help! The numbers show no sign of slowing down either.

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