Research On Movement Trajectory Detection And Application In Friend Recommendation Based On Social Media

Location information is very important in the real world. How to detect users’ locations and further movement trajectories automatically is significant for many location based services such as dietary recommendation and tourism planning. With the rapid development of social media such as Sina Weibo and Twitter, more and more people publish posts at any time which contain their real-time location information. This makes it possible to detect users’movement trajectories automatically by social media. This paper majors in movement trajectory detection and friend recommendation. As an application, friend recommendation 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. However, friends recommended by these methods are only suitable for virtual social space instead of the real world.This thesis contains location detection, movement trajectory detection and friend recommendation. Firstly, on the aspect of location detection, it combines two components, i.e. a Bayes model based on words distribution over locations and the social relationships of users in social media, to detect or infer users’locations. The former one is utilized to judge whether the content of a post contains explicit/implicit location information and further to detect the location. The latter one assumes that friends tend to gather together when they talk about the same thing. Thus, it infers a user’s locations from his friends’detected locations in social media. Secondly, movement trajectory detection, it constructs the user’s movement trajectory by smoothing these detected locations in first phase according to both semantic context of posts and transfer time between inferred locations. Lastly, we also put forward a location sensitive friend recommendation model 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. 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 methods no matter in movement trajectory detection or friend recommendation.