Two-tier weighting aggregation ranking algorithm

Abstract

In ranking for document retrieval,queries often vary greatly from one another.However,most of the existing ranking methods do not consider significant differences between queries.Correctly ranking documents on the top of the result list is crucial,and one must conduct training in a way that such ranked results are accurate.A two-tier weighting aggregation ranking method was proposed.This method consisted of two steps,training of base rankers and query-level ranker aggregation.First,base rankers were established based on each query,assigning asymmetric weights to its relevant documents,then,query-level ranker aggregation used a supervised approach to learn query-dependent weights when these base rankers were combined.The experimental results on the benchmark data set LETRO ONHSUMED show that the ranking performance has been significantly improved.