Preventing Overfitting via Sample Reweighting for Recommender System Incremental Update (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v35i18.17928Keywords:
Recommender System, Incremental Update, Sample ReweightingAbstract
Incremental update of recommender system models using only newly arrived data may easily cause the model to overfit to the current data. To address this issue without relying on historical data, we propose a sample reweighting method based on prediction performance of previous model on current data. The proposed method effectively alleviates the problem of overfitting and improves the performance of incremental update.Downloads
Published
2021-05-18
How to Cite
Peng, D., Hu, X., Zeng, A., & Zhang, J. (2021). Preventing Overfitting via Sample Reweighting for Recommender System Incremental Update (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15863-15864. https://doi.org/10.1609/aaai.v35i18.17928
Issue
Section
AAAI Student Abstract and Poster Program