LOCAl: a personalized cache mechanism for location-based social networks

Abstract

Recommending nearby Points of Interest (POI) has received growing interest in mobile location-based networks today, where users share content embedded with location information. In this work, we propose a novel caching framework to support personalised proactive caching for mobile location-based social networks. We propose "LOCAI", which uses a probabilistic approach in order to predict the POIs that users will access and retrieve the appropriate data objects that will fulfill user preferences. Our detailed experimental evaluation, using data from the Foursquare location-based social network, illustrates that LOCAI minimizes the user latency to retrieve the data objects they are interested in, is efficient and practical.