Spatial probabilistic modeling of calls to businesses

Abstract

Local search engines allow users to search for entities such as businesses in a particular geographic location. To improve the geographic relevance of search, user feedback data such as logged click locations are traditionally used. In this paper, we use anonymized mobile call log data as an alternate source of data and investigate its relevance to local search. Such data consists of records of anonymized mobile calls made to local businesses along with the locations of celltowers that handled the calls. We model the probability of calls made to particular categories of businesses as a function of distance, using a generalized linear model framework. We provide a detailed comparison between a click log and a mobile call log, showing its relevance to local search. We describe our probabilistic models and apply them to anonymized mobile call logs for New York City and Los Angeles restaurants.

Explaining the product range effect in purchase data

“…This line of research is present also on the computer science side [10]. In computer science, there are also examples of GIS approaches to business intelligence [11], of recommender systems for customer retention [12] and spatial analysis of customer-to-business communications [13]. In computer science [14] is a comprehensive book explaining the relations between economy and a network-based analysis.…”