Journal
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
Volume 8, Issue 3, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2963105
Keywords
Algorithms; Experimentation; Performance; Learning user attributes; mobile social multimedia analytic; occupation inference
Funding
- Qilu Scholar grant
Ask authors/readers for more resources
Learning user attributes from mobile social media is a fundamental basis for many applications, such as personalized and targeting services. A large and growing body of literature has investigated the user attributes learning problem. However, far too little attention has been paid to jointly consider the dual heterogeneities of user attributes learning by harvesting multiple social media sources. In particular, user attributes are complementarily and comprehensively characterized by multiple social media sources, including footprints from Foursqare, daily updates from Twitter, professional careers from Linkedin, and photo posts from Instagram. On the other hand, attributes are inter-correlated in a complex way rather than independent to each other, and highly related attributes may share similar feature sets. Towards this end, we proposed a unified model to jointly regularize the source consistency and graph-constrained relatedness among tasks. As a byproduct, it is able to learn the attribute-specific and attribute-sharing features via graph-guided fused lasso penalty. Besides, we have theoretically demonstrated its optimization. Extensive evaluations on a real-world dataset thoroughly demonstrated the effectiveness of our proposed model.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available