Preventing Overfitting via Sample Reweighting for Recommender System Incremental Update (Student Abstract)
Keywords:Recommender System, Incremental Update, Sample Reweighting
AbstractIncremental 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.
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. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17928
AAAI Student Abstract and Poster Program