OmniSuggest: A Ubiquitous Cloud-Based Context-Aware Recommendation System for Mobile Social Networks

Abstract

The evolution of mobile social networks and the availability of online check-in services, such as Foursquare and Gowalla, have initiated a new wave of research in the area of venue recommendation systems. Such systems recommend places to users closely related to their preferences. Although venue recommendation systems have been studied in recent literature, the existing approaches, mostly based on collaborative filtering, suffer from various issues, such as: 1) data sparseness, 2) cold start, and 3) scalability. Moreover, many existing schemes are limited in functionality, as the generated recommendations do not consider group of “friends” type situations. Furthermore, the traditional systems do not take into account the effect of real-time physical factors (e.g., distance from venue, traffic, and weather conditions) on recommendations. To address the aforementioned issues, this paper proposes a novel cloud-based recommendation framework OmniSuggest that utilizes: 1) Ant colony algorithms, 2) social filtering, and 3) hub and authority scores, to generate optimal venue recommendations. Unlike existing work, our approach suggests venues at a finer granularity for an individual or a “group” of friends with similar interest. Comprehensive experiments are conducted with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than many state of the art schemes.