fajieyuan/recommendation_transfer_learning_pretraining
Pre-training and Transfer learning papers for recommendation
This project helps researchers and practitioners in recommendation systems by providing a curated list of academic papers focused on using pre-training and transfer learning techniques. It allows them to understand how to leverage existing knowledge to build better recommender systems, especially when data is scarce or users are new. The end-users are researchers, data scientists, and ML engineers working on recommender systems.
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Use this if you are a researcher or practitioner in recommender systems looking for academic literature and resources on applying pre-training and transfer learning to improve recommendation quality, particularly for cold-start users or tasks with limited data.
Not ideal if you are looking for a ready-to-use software library or tool to directly implement a recommendation system without delving into academic papers and research methodologies.
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Mar 09, 2024
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