Location disambiguation in local searches using gradient boosted decision trees

Local search is a specialization of the web search that allows users to submit geographically constrained queries. However, one of the challenges for local search engines is to uniquely understand and locate the geographical intent of the query. Geographical constraints (or location references) in a local search are often incomplete and thereby suffer from the referent ambiguity problem where the same location name can mean several different possibilities. For instance, just the term “Springfield” by itself can refer to 30 different cities in the USA. Previous approaches to location disambiguation have generally been hand compiled heuristic models. In this paper, we examine a data-driven, machine learning approach to location disambiguation. Essentially, we separately train a Gradient Boosted Decision Tree (GBDT) model on thousands of desktop and mobile-based local searches and compare the performance to one of our previous heuristic based location disambiguation system (HLDS). Th...

• Type: Journal
• Year: 2010
• Where: GIS
• Authors: Ritesh Agrawal, James G. Shanahan
• Keywords: Automated Reasoning, GIS 2010, Local Search, Location Disambiguation, Mobile-based Local Searches