Winner of the "Outstanding Academic Title" recognition by Choice for the 2020 OAT Awards.
The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community.
In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem.
- Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity
- Presents a detailed study of existing research
- Provides convergence and complexity analysis of the frameworks
- Includes algorithms to implement the proposed research work
- Covers extensive empirical analysis
Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.
Table of Contents
1. Introduction. 2. Related Work. 3. User-Guided Cross-Domain Sentiment Classification. 4. Similar Actor Recommendation. 5. Source-Free Domain Adaptation of the Off-the-Shelf Classifier. 6. Social Media for Diabetes Management. 7. Conclusion and Future Work.
Arun Reddy Nelakurthi is a Senior Engineer in Machine Learning Research at Samsung Research America, Mountain View, California. He received his PhD in Machine Learning from Arizona State University in 2019. His research focuses on heterogeneous machine learning, transfer learning, user modeling and semi-supervised learning, with applications in social network analysis, social media analysis and healthcare informatics. He has served on the program committee for Conference on Information and Knowledge Management (CIKM) and The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). He also worked as a reviewer for IEEE Transactions on Knowledge and Data Engineering (TKDE), Data Mining and Knowledge Discovery (DMKD) and IEEE Transactions on Neural Networks and Learning Systems (TNNLS) journals.
Jingrui He is an associate professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign. She received her PhD in machine learning from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, rare category analysis, active learning and semi-supervised learning, with applications in social network analysis, healthcare, and manufacturing processes. Dr. He is the recipient of the 2016 NSF CAREER Award and a threetime recipient of the IBM Faculty Award, in 2018, 2015 and 2014 respectively. She was selected for an IJCAI 2017 Early Career Spotlight, and was invited to the 24th CNSF Capitol Hill Science Exhibition. Dr. He has published more than 90 refereed articles, and is the author of the book, Analysis of Rare Categories (Springer- Verlag, 2011). Her papers have been selected as ”Best of the Conference” by ICDM 2016, ICDM 2010, and SDM 2010. She has served on the senior program committee/ program committee for Knowledge Discovery and Data Mining (KDD), International Joint Conference on Artificial Intelligence (IJCAI), Association for the Advancement of Artificial Intelligence (AAAI), SIAM International Conference on Data Mining (SDM), and International Conference on Machine Learning (ICML).