Abstract: A training system is described for generating at least one ranking module using features derived, in part, from region information. The region information encodes characteristics about regions which are associated with queries in search log data. A query processing system is also described for applying the ranking model generated by the training system to process queries in real time. In one implementation, the training system can also generate plural ranking models corresponding to plural respective map areas. The training system can also generate a mapping model which correlates each region with a ranking model to be applied when processing queries that originate from that region. The query processing system can process a query by determining a region associated with the query and then identifying and applying a ranking model which corresponds to the region.

Abstract: A local search system is described herein that provides a framework for the integration of various external sources to improve local search ranking. The framework provided by the local search system described herein uses a notion of backoff. The system uses a generalization of the concept of backoff to improve local search results that incorporate a variety of data features. The system can apply backoff in multiple dimensions at the same time to generate features for local search ranking. The system integrates various additional data sources, such as web access logs, driving direction request logs, reviews, and so forth, to quantify popularity and distance (or distance sensitivity) into a framework for local search ranking. Thus, the system provides search results that are more relevant by incorporating a number of data sources into the ranking in a manner that handles abnormalities in the data well.