How to Retrain Recommender System? A Sequential Meta-Learning Method

27 May 2020Yang ZhangFuli FengChenxu WangXiangnan HeMeng WangYan LiYongdong Zhang

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference... (read more)

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