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.
Features:
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.
| Preface |
|
ix | |
| Acknowledgment |
|
xi | |
| Authors |
|
xiii | |
| Contributors |
|
xv | |
|
|
|
1 | (4) |
|
Chapter 2 Literature Survey |
|
|
5 | (6) |
|
2.1 Impact of Social Media |
|
|
5 | (1) |
|
2.2 Heterogeneous Learning and Social Media |
|
|
6 | (3) |
|
2.2.1 Transductive Transfer Learning |
|
|
7 | (1) |
|
2.2.2 Source-free Transfer Learning |
|
|
8 | (1) |
|
2.2.3 Identifying Similar Actors Across Networks |
|
|
8 | (1) |
|
2.3 Explaining Task Heterogeneity |
|
|
9 | (2) |
|
Chapter 3 Social Media for Diabetes Management |
|
|
11 | (8) |
|
|
|
11 | (1) |
|
|
|
12 | (3) |
|
|
|
15 | (1) |
|
3.4 Challenges in Real-World Applications |
|
|
16 | (3) |
|
Chapter 4 Learning from Task Heterogeneity |
|
|
19 | (44) |
|
4.1 Cross-Domain User Behavior Modeling |
|
|
19 | (14) |
|
|
|
20 | (1) |
|
|
|
20 | (1) |
|
4.1.1.2 User-Example-Feature Tripartite Graph |
|
|
21 | (2) |
|
4.1.1.3 Objective Function |
|
|
23 | (1) |
|
4.1.1.4 User Soft-Score Weights |
|
|
24 | (1) |
|
4.1.1.5 U-Cross Algorithm |
|
|
24 | (4) |
|
|
|
28 | (1) |
|
|
|
29 | (1) |
|
|
|
29 | (1) |
|
|
|
30 | (1) |
|
4.1.3.3 Empirical Analysis |
|
|
31 | (2) |
|
4.2 Similar Actor Recommendation |
|
|
33 | (11) |
|
|
|
35 | (1) |
|
4.2.1.1 Notation and Problem Definition |
|
|
35 | (1) |
|
|
|
36 | (1) |
|
4.2.2.1 Matrix Factorization for Cross Network Link Recommendation |
|
|
36 | (2) |
|
4.2.2.2 Proposed Framework |
|
|
38 | (1) |
|
4.2.2.3 Optimization Algorithm |
|
|
39 | (2) |
|
4.2.2.4 Link Recommendation |
|
|
41 | (1) |
|
4.2.2.5 Complexity Analysis |
|
|
41 | (1) |
|
|
|
42 | (1) |
|
|
|
42 | (1) |
|
|
|
43 | (1) |
|
|
|
44 | (1) |
|
4.3 Source-Free Domain Adaptation |
|
|
44 | (19) |
|
|
|
45 | (1) |
|
|
|
46 | (1) |
|
|
|
47 | (3) |
|
4.3.2.2 Distribution Shift |
|
|
50 | (1) |
|
4.3.2.3 Convergence of AOT |
|
|
51 | (1) |
|
|
|
52 | (6) |
|
4.3.3.1 Two Stage Analysis |
|
|
58 | (1) |
|
4.3.3.2 Sensitivity Analysis |
|
|
58 | (2) |
|
4.3.3.3 Convergence Analysis |
|
|
60 | (1) |
|
|
|
61 | (2) |
|
Chapter 5 Explainable Transfer Learning |
|
|
63 | (18) |
|
|
|
65 | (9) |
|
|
|
65 | (1) |
|
|
|
65 | (1) |
|
5.1.3 Reweighting the Source Domain Examples |
|
|
66 | (2) |
|
5.1.4 Domain Invariant Representation |
|
|
68 | (2) |
|
|
|
70 | (2) |
|
5.1.6 Shallow Neural Network: An Example |
|
|
72 | (2) |
|
|
|
74 | (7) |
|
|
|
74 | (2) |
|
|
|
76 | (5) |
|
|
|
81 | (6) |
|
6.1 User Behavior Modeling in Social Media |
|
|
81 | (1) |
|
6.2 Addressing and Explaining Task Heterogeneity |
|
|
82 | (1) |
|
|
|
83 | (2) |
|
6.3.1 Addressing Concept Drift |
|
|
83 | (1) |
|
|
|
83 | (1) |
|
|
|
84 | (1) |
|
6.3.4 Ethical Issues in Healthcare |
|
|
84 | (1) |
|
6.3.5 Misinformation and Disinformation in Healthcare |
|
|
85 | (1) |
|
|
|
85 | (2) |
| Bibliography |
|
87 | (10) |
| Index |
|
97 | |
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).
Rohkem infot Taylor & Francis e-raamatute kohta