Weight Loss Prediction in Social-Temporal Context

Mobile applications for weight loss are becoming increasingly popular due to their cost-effective and wide-reaching characteristics. The self-monitoring function in these applications plays a crucial role in gaining success of weight loss intervention, as it can give tailored feedback and motivation enhancement to users. Thus, by shortening such self-reinforcement cycle and providing timely feedback at much earlier stage, precisely predicting user's future weight will significantly improve the effectiveness of existing applications. In this paper, we investigate the weight loss prediction problem by leveraging social-temporal information, which is abundant and ubiquitous in weight loss applications. The study is based on a large-scale dataset from a weight loss application BOOHEE, which contains more than 9 million users with a timespan of multiple years (from 2012 to 2015). Our analysis validates the existence of social-temporal correlations in weight loss and modeling these correlations leads to a novel weight LOss prediction framework in Social-Temporal context LOST. The experimental results on the real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of social-temporal correlations in weight loss prediction.