Preventing Overfitting via Sample Reweighting for Recommender System Incremental Update (Student Abstract)

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....

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Bibliographic Details
Main Authors: Peng, Danni, Hu, Xiaobo, Zeng, Anxiang, Zhang, Jie
Format: Conference Proceeding
Language:English
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Summary: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.
ISSN:2159-5399
2374-3468
DOI:10.1609/aaai.v35i18.17928