论文标题
通过预培训进行推荐的知识转移：审查和潜在客户
Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect
论文作者
论文摘要
推荐系统的目的是为用户提供项目建议，并且通常面临着现实情况下的数据稀疏问题（例如，冷启动）。最近，预训练的模型显示了它们在域和任务之间的知识转移方面的有效性，这可能会缓解推荐系统中的数据稀疏问题。在这项调查中，我们首先对具有预训练的推荐系统进行审查。此外，我们通过实验展示了预培训对推荐系统的好处。最后，我们讨论了一些有前途的研究指导，以供预先培训的推荐系统进行未来的研究。
Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (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 for recommender systems with pre-training.
