Learning similarity function for rare queries
US8612367B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 4, 2011 |
| Grant date | Dec 17, 2013 |
| Priority date | — |
| Expiry date | Jan 27, 2032 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L9/3236
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Techniques are described for determining queries that are similar to rare queries. An n-gram space is defined to represent queries and a similarity function is defined to measure the similarities between queries. The similarity function is learned by leveraging training data derived from user behavior data and formalized as an optimization problem using a metric learning approach. Furthermore, the similarity function can be defined in the n-gram space, which is equivalent to a cosine similarity in a transformed n-gram space. Locality sensitive hashing can be exploited for efficient retrieval of similar queries from a large query repository. This technique can be used to enhance the accuracy of query similarity calculation for rare queries, facilitate the retrieval of similar queries and significantly improve search relevance.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.