Location Sensitive Friend Recommendation in Social Network

Abstract

How to recommend friends in social network has attracted many research efforts. Most current friend recommendation methods are just based on the assumption that people will become friends if they have common interests which are usually estimated with the contents of their published posts and following relationships. However, friends recommended by these methods are only suitable for virtual social space instead of the real world. In this paper, we propose a new method to recommend friends in social network from the perspective of not just common interests, but also real-life needs. That is, we focus on finding friends that they can communicate with each other by social network and participate in some real-life activities face to face. The central idea of our approach is that we suppose people are more likely to be friends if their lives share more location overlaps besides the common interests. Currently, most people publish posts containing their real-time location information at any time, which makes it possible to detect and use the location information to recommend friends. Thus, our method combines users’ published posts, their location sequences detected from the posts and how active they are in Sina Weibo to estimate whether they can become friends in not only social network but also the real world. Experiments on Sina Weibo dataset demonstrate that our method can significantly outperform the traditional friend recommendation methods in terms of Precision, Recall and F1 measures.