Users increasingly rely on their mobile devices to search local entities, typically businesses, while on the go. Even though recent work has recognized that the ranking signals in mobile local search (e.g., distance and rating score of a business) are quite different from general Web search, they have mostly treated these signals as a black-box to extract very basic features (e.g., raw distance values and raw rating scores) without going inside the signals to understand how exactly they affect user click behaviors. However, it is critical to explore the underlying behaviors/heuristics of a ranking signal so as to design more effective ranking features; this has been demonstrated extensively in the development of general information retrieval models.
In this paper, we follow a data-driven methodology to study the behavior of these ranking signals in mobile local search using a large-scale query log. Our analysis reveals interesting heuristics that can be used to guide the exploitation of different signals. For example, users often take the mean value of a signal (e.g., rating) from the business result list as a “pivot” score, and tend to demonstrate different click behaviors on businesses with lower and higher signal values than the pivot; the clickrate of a business generally is sublinearly decreasing with its distance to the user, etc. Inspired by the understanding of these heuristics, we further propose different normalization methods to generate more effective ranking features. We quantify the improvement of the proposed new features using real mobile local search logs over a period of 14 months and show that the mean average precision can be improved significantly by over 7%.