MobiFeed: A location-aware news feed framework for moving users

Abstract

A location-aware news feed system enables mobile users to share geo-tagged user-generated messages, e.g., a user can receive nearby messages that are the most relevant to her. In this paper, we present MobiFeed that is a framework designed for scheduling news feeds for mobile users. MobiFeed consists of three key functions, <i>location prediction</i>, <i>relevance measure</i>, and <i>news feed scheduler</i>. The <i>location prediction</i> function is designed to estimate a mobile user’s locations based on a path prediction algorithm. The <i>relevance measure</i> function is implemented by combining the vector space model with non-spatial and spatial factors to determine the relevance of a message to a user. The <i>news feed scheduler</i> works with the other two functions to generate news feeds for a mobile user at her current and predicted locations with the best overall quality. We propose a heuristic algorithm as well as an optimal algorithm for the location-aware <i>news feed scheduler</i>. The performance of MobiFeed is evaluated through extensive experiments using a real road map and a real social network data set. The scalability of MobiFeed is also investigated using a synthetic data set. Experimental results show that MobiFeed obtains a relevance score two times higher than the state-of-the-art approach, and it can scale up to a large number of geo-tagged messages.