Query selection for effectively learning ranking functions
US8112421B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 20, 2007 |
| Grant date | Feb 7, 2012 |
| Priority date | — |
| Expiry date | May 29, 2028 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F16/334
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A learning system for a search ranking function model may include a computer program that iteratively refines the model using new queries and associated documents from an unlabeled training set. The unlabeled training set may include a set of queries for which the associated documents have not been labeled as “relevant” or otherwise labeled. The new queries may be selected based on a similarity to and an accuracy of each neighbor from a labeled training set, such as a labeled validation set. Upon selection, the documents associated with the new queries may be labeled. The new queries and their associated documents may be accumulated into a labeled training set, such as a labeled training set, and a refined model may be learned based on the augmented labeled training set. The model may be iteratively refined until it is determined that the model is adequate.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.