MPG: Not So Random Exploration of a City

摘要

The proliferation of mobile, ubiquitous and spatial computing has led to a number of services aiming into facilitate the exploration of a city. Platforms such as Foursquare and Yelp curate information about establishments in an area that can then be used for recommendation purposes. Traditionally an approach followed by these systems is to rank places based on their popularity, proximity or any other feature that represents the quality of the venue and then return the top-k of them. However, this approach, while simple and intuitive, is not necessarily providing a diverse set of recommendations, since similar venues typically are ranked closely. Therefore, in this paper we design and introduce MPG (which stands for Mobile Personal Guide), a mobile service that provides a set of diverse venue recommendations better aligned with user preferences. MPG takes into consideration the user preferences (e.g., distance willing to cover, types of venues interested in exploring, etc.), the popularity of the establishments, as well as their distance from the current location of the user by combining them in a single composite score. We evaluate our approach using a large-scale dataset of approximately 14 million venues collected from Foursquare. Our results indicate that MPG can increase coverage of the result set compared to the baselines considered. It further achieves a significantly better Relevancy-Diversity trade-off ratio.