A Latent Source Model for Online Collaborative Filtering
Despite the prevalence of collaborative filtering in recommendation systems, there has been little theoretical development on why and how well it works, especially in the “online” setting, where items are recommended to users over time. We address this theoretical gap by introducing a model for onli...
Main Authors: | Bresler, Guy, Chen, George, Shah, Devavrat |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
Format: | Article |
Language: | en_US |
Published: |
Neural Information Processing Systems Foundation, Inc.
2014
|
Online Access: | http://hdl.handle.net/1721.1/91678 https://orcid.org/0000-0003-0737-3259 https://orcid.org/0000-0003-1303-582X |
Similar Items
-
Matrix Estimation, Latent Variable Model and Collaborative Filtering
by: Shah, Devavrat
Published: (2021) -
Matrix Estimation, Latent Variable Model and Collaborative Filtering
by: Shah, Devavrat
Published: (2021) -
Collaborative Filtering with Low Regret
by: Bresler, Guy, et al.
Published: (2021) -
A Latent Source Model for Patch-Based Image Segmentation
by: Shah, Devavrat, et al.
Published: (2018) -
Blind regression: Nonparametric regression for latent variable models via collaborative filtering
by: Shah, Devavrat, et al.
Published: (2021)