Learning to Blend Rankings: A Monotonic Transformation to Blend Rankings from Heterogeneous Domains

摘要

There have been great needs to develop effective methods for combining multiple rankings from heterogeneous domains into one single rank list arising from many recent web search applications, such as integrating web search results from multiple engines, facets, or verticals. We define this problem as Learning to blend rankings from multiple domains. We propose a class of learning-to-blend methods that learn a monotonically increasing transformation for each ranking so that the rank order in each domain is preserved and the transformed values are comparable across multiple rankings. The transformation learning can be tackled by solving a quadratic programming problem. The novel machine learning method for blending multiple ranking lists is evaluated with queries sampled from a commercial search engine and a promising improvement of Discounted Cumulative Gain has been observed.