Paper Info
Title
TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning.
Abstract
Wellness is a widely popular concept that is commonly applied to fitness and self-help products or services. Inference of personal wellness-related attributes, such as body mass index or diseases tendency, as well as understanding of global dependencies between wellness attributes and users' behavior is of crucial importance to various applications in personal and public wellness domains. Meanwhile, the emergence of social media platforms and wearable sensors makes it feasible to perform wellness profiling for users from multiple perspectives. However, research efforts on wellness profiling and integration of social media and sensor data are relatively sparse, and this study represents one of the first attempts in this direction. Specifically, to infer personal wellness attributes, we proposed multi-source individual user profile learning framework named "TweetFit". "TweetFit" can handle data incompleteness and perform wellness attributes inference from sensor and social media data simultaneously. Our experimental results show that the integration of the data from sensors and multiple social media sources can substantially boost the wellness profiling performance.
Year
Venue
Field
2017
THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Social network,User profile,Social media,Multi-task learning,Profiling (computer programming),Inference,Computer science,Wearable computer,Human–computer interaction,Artificial intelligence,Multimedia,Machine learning
DocType
Citations 
PageRank 
Conference
4
0.40
References 
Authors
0
2
Name
Order
Citations
PageRank
Aleksandr Farseev1927.48
Tat-Seng Chua211749653.09