In reality, users have different interests in different periods, regions, scenes, etc. Such changes in interest are so drastic that they are difficult to be captured by recommenders. Existing multi-domain learning can alleviate this problem. However, the structure of the industrial recommendation system is complex, the amount of data is huge, and the training cost is extremely high, so it is difficult to modify the structure of the industrial recommender and re-train it. To fill this gap, we consider recommenders as large pre-trained models and fine-tune them. We first propose the theory of the information bottleneck for fine-tuning and present an explanation for the fine-tuning technique in recommenders. To tailor for recommendation, we design an information-aware adaptive kernel (IAK) technique to fine-tune the pre-trained recommender. Specifically, we define fine-tuning as two phases: knowledge compression and knowledge matching and let the training stage of IAK explicitly approximate these two phases. Our proposed approach designed from the essence of fine-tuning is well interpretable. Extensive online and offline experiments show the superiority of our proposed method. Besides, we also share unique and important lessons we learned when deploying the method in a large-scale online platform. We also present the potential issues of fine-tuning techniques in recommendation systems and the corresponding solutions. The recommender with IAK technique has been deployed on the homepage of a billion-scale online food platform for several months and has yielded considerable profits in our business.

翻译：现实中，用户在不同时段、地域、场景下的兴趣偏好存在显著差异。此类兴趣变化极为剧烈，传统推荐系统难以有效捕捉。现有的多领域学习方法虽能缓解此问题，但工业推荐系统结构复杂、数据量庞大、训练成本极高，难以对其架构进行修改并重新训练。为填补这一空白，我们将推荐系统视为大型预训练模型进行微调。首先提出面向微调的信息瓶颈理论，为推荐系统中的微调技术提供理论解释。针对推荐任务特性，我们设计了一种信息感知自适应核（IAK）技术来微调预训练推荐模型。具体而言，我们将微调过程定义为知识压缩与知识匹配两个阶段，并通过IAK的训练过程显式逼近这两个阶段。这种从微调本质出发的设计具有良好可解释性。大量线上与线下实验证明了所提方法的优越性。此外，我们还分享了在大规模在线平台部署该方法时获得的独特且重要的实践经验，同时探讨了推荐系统中微调技术可能存在的问题及相应解决方案。搭载IAK技术的推荐系统已在亿级规模在线餐饮平台首页持续部署数月，为业务带来了可观收益。