Tensor Factorization Recommender Systems with Dependency
Zhang, J., Yuan, Y., & Qu, A. (2022). Tensor factorization recommender systems with dependency. Electronic Journal of Statistics, 16, 2175-2205.
CV
Education
- B.S. in GitHub, GitHub University, 2012
- M.S. in Jekyll, GitHub University, 2014
- Ph.D in Version Control Theory, GitHub University, 2018 (expected)
Work experience
- Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
- Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
Individualized Dynamic Model for Multi-resolutional Data with Application to Mobile Health
Zhang, Jiuchen, et al. "Individualized Dynamic Latent Factor Model with Application to Mobile Health Data." arXiv preprint arXiv:2311.12392 (2023).
Talks
# # Multilayer Recommender Systems Using Dependent Tensor #
Contributed Talk at Joint Statistical Meetings (JSM), Philadelphia, PA
# # A Tensor Factorization Recommender System with Dependency #
Invited Talk at 2021 ICSA Applied Statistics Symposium, Virtual
# # Individualized Dynamic Latent Factor Model with Application to Mobile Health Data #
Contributed Talk at Joint Statistical Meetings (JSM), Toronto, Canada
# # Individualized Dynamic Latent Factor Model for Multi-resolutional Data with Application to Mobile Health #
Contributed Talk at Eastern North American Region (ENAR) Spring Meeting, Baltimore , MA
Teaching
Service and leadership
- Currently signed in to 43 different slack teams