Crowds replace experts: Building better location-based services using mobile social network interactions

TLDR

Location‑based services are increasingly popular, yet their relevance is challenged by rapidly changing data and limited mobile screens, making mobile social networks a promising source for improved services. This work introduces SocialTelescope, a system that automatically compiles, indexes, and ranks locations using mobile social network interactions. SocialTelescope is implemented as a search engine that learns about places from geo‑tweets and its coverage and relevance were evaluated against Google Local Search, Zagat, and Yelp. The system achieves relevance comparable to Google Local Search, Zagat, and Yelp while operating at substantially lower cost.

Abstract

Location-based services are growing in popularity due to the ubiquity of smartphone users. The relevance of location-based query results is very important, especially for mobile phones with limited screen size. Location-based data frequently changes; this introduces challenges in indexing and ranking places. The growing popularity of mobile social networks, such as Twitter, FourSquare and Facebook Places, presents an opportunity to build better location-based services by leveraging user interactions on these networks. In this paper, we present SocialTelescope, a location-based service that automatically compiles, indexes and ranks locations, based on user interactions with locations in mobile social networks. We implemented our system as a location-based search engine that uses geo-tweets by Twitter users to learn about places. We evaluated the coverage and relevance of our system by comparing it against current state-of-the-art approaches including page-rank (Google Local Search), expert-based (Zagat) and user-review based (Yelp). Our results show that a crowd-sourced location-based service returns results that match those returned by current approaches in relevance, at a substantially lower cost.