DISGD: A Distributed Shared-nothing Matrix Factorization for Large Scale Online Recommender Systems
With the web-scale data volumes and high velocity of generation rates, it has become crucial that the training process for recom mender systems be a continuous process which is performed on live data, i.e., on data streams. In practice, such systems have to address three main requirements including...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , |
| Published: |
2020
|
| Online Access: | https://bspace.buid.ac.ae/handle/1234/2927 https://openproceedings.org/2020/conf/edbt/paper_200.pdf https://doi.org/10.5441/002/edbt.2020.32 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|