Improving Local Search with Open Geographic Data

Abstract

Local search helps users find certain types of business units (restaurant, gas stations, hospitals, etc.) in the surrounding area. However, some merchants might not have much online content (e.g. customer reviews, business descriptions, opening hours, telephone numbers, etc.). This can pose a problem for traditional local search algorithms such as vector space based approaches. With this difficulty in mind, in this paper we present an approach to local search that incorporates geographic open data. Using the publicly available {\em Yelp} dataset we are able to uncover patterns that link geographic features and user preferences. From this, we propose a model to infer user preferences that integrates geographic parameters. Through this model and estimation of user preference, we develop a new framework for ``local'' (in the sense of geography) search that offsets the absence of contexts regarding physical business units. Our initial analysis points to the meaningful integration of open geographic data in local search and points out several directions for further research.