US 2010/0250523 A1 · SYSTEM AND METHOD FOR LEARNING A RANKING MODEL THAT OPTIMIZES A RANKING EVALUATION METRIC FOR RANKING SEARCH RESULTS OF A SEARCH QUERY

Abstract

An improved system and method for learning a ranking model that optimizes a ranking evaluation metric for ranking search results of a search query is provided. An optimized nDCG ranking model that optimizes an approximation of an average nDCG ranking evaluation metric may be generated from training data through an iterative boosting method for learning to more accurately rank a list of search results for a query. A combination of weak ranking classifiers may be iteratively learned that optimize an approximation of an average nDCG ranking evaluation metric for the training data by training a weak ranking classifier at each iteration for each document in the training data with a computed weight and assigned class label, and then updating the optimized nDCG ranking model by adding the weak ranking classifier with a combination weight to the optimized nDCG ranking model.

Claims20

Dependent claims

A computer system for ranking search results of a search query, comprising:

an optimized nDCG ranking model generator that optimizes an nDCG ranking evaluation metric to generate from a plurality of sets of training data, each set including at least one training search query and at least one ranked list of documents, a nDCG ranking model that ranks a list of search results of a search query;
and
a storage, operably coupled to the optimized nDCG ranking model generator, that stores the optimized nDCG ranking model and the plurality of sets of training data.

Dependencies are inferred from the claim text.

System and method for learning a ranking model that optimizes a ranking evaluation metric for ranking search results of a search query

Life of this patent

System and method for learning a ranking model that optimizes a ranking evaluation metric for ranking search results of a search query

Life of this patent

Abstract

Claims20