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• W3136606064 abstract "Recommender systems aim to provide item recommendations for users and are usually faced with data sparsity problems (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems. In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments. Finally, we discuss several promising directions for future research of recommender systems with pre-training. The source code of our experiments will be available to facilitate future research." @default.
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• W3136606064 date "2021-03-18" @default.
• W3136606064 modified "2023-10-16" @default.
• W3136606064 title "Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect" @default.
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• W3136606064 doi "https://doi.org/10.3389/fdata.2021.602071" @default.
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