Semi-Discrete Social Recommendation (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v35i18.17914Keywords:
Social Recommendation, Matrix Factorization, Network Embedding, Learning To HashAbstract
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.Downloads
Published
2021-05-18
How to Cite
Luo, F., Wu, J., & Wang, H. (2021). Semi-Discrete Social Recommendation (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15835-15836. https://doi.org/10.1609/aaai.v35i18.17914
Issue
Section
AAAI Student Abstract and Poster Program