Click through rate prediction for local search results

With the ubiquity of internet access and location services provided by smartphone devices, the volume of queries issued by users to find products and services that are located near them is rapidly increasing. Local search engines help users in this task by matching queries with a predefined geographical connotation ("local queries") against a database of local business listings. Local search differs from traditional web-search because to correctly capture users' click behavior, the estimation of relevance between query and candidate results must be integrated with geographical signals, such as distance. The intuition is that users prefer businesses that are physically closer to them. However, this notion of closeness is likely to depend upon other factors, like the category of the business, the quality of the service provided, the density of businesses in the area of interest, etc. In this paper we perform an extensive analysis of online users' behavior and investigate the problem of estimating the click-through rate on local search (LCTR) by exploiting the combination of standard retrieval methods with a rich collection of geo and business-dependent features. We validate our approach on a large log collected from a real-world local search service. Our evaluation shows that the non-linear combination of business information, geo-local and textual relevance features leads to a significant improvements over state of the art alternative approaches based on a combination of relevance, distance and business reputation