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


View PDF

WebFX Careers

Join our mission to provide industry-leading digital marketing services to businesses around the globe - all while building your personal knowledge and growing as an individual.

We're Hiring!
View 30+ job openings!