Semi-Discrete Social Recommendation (Student Abstract)
Abstract
Combining matrix factorization (MF) with network embedding (NE) has been a promising solution to social recommender systems. However, such a scheme suffers from the online predictive efficiency issue due to the ever-growing users and items. In this paper, we propose a novel hashing-based social recommendation model, called semi-discrete socially embedded matrix factorization (S2MF), which leverages the dual advantages of social information for recommendation effectiveness and hashing trick for online predictive efficiency. Experimental results demonstrate the advantages of S2MF over state-of-the-art discrete recommendation models and its real-valued competitors.
Cite
Text
Luo et al. "Semi-Discrete Social Recommendation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17914Markdown
[Luo et al. "Semi-Discrete Social Recommendation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/luo2021aaai-semi/) doi:10.1609/AAAI.V35I18.17914BibTeX
@inproceedings{luo2021aaai-semi,
title = {{Semi-Discrete Social Recommendation (Student Abstract)}},
author = {Luo, Fangyuan and Wu, Jun and Wang, Haishuai},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2021},
pages = {15835-15836},
doi = {10.1609/AAAI.V35I18.17914},
url = {https://mlanthology.org/aaai/2021/luo2021aaai-semi/}
}