Current recommendation systems are significantly affected by a serious issue of temporal data shift, which is the inconsistency between the distribution of historical data and that of online data. Most existing models focus on utilizing updated data, overlooking the transferable, temporal data shift-free information that can be learned from shifting data. We propose the Temporal Invariance of Association theorem, which suggests that given a fixed search space, the relationship between the data and the data in the search space keeps invariant over time. Leveraging this principle, we designed a retrieval-based recommendation system framework that can train a data shift-free relevance network using shifting data, significantly enhancing the predictive performance of the original model in the recommendation system. However, retrieval-based recommendation models face substantial inference time costs when deployed online. To address this, we further designed a distill framework that can distill information from the relevance network into a parameterized module using shifting data. The distilled model can be deployed online alongside the original model, with only a minimal increase in inference time. Extensive experiments on multiple real datasets demonstrate that our framework significantly improves the performance of the original model by utilizing shifting data.

翻译：当前推荐系统受到时间数据偏移（即历史数据分布与在线数据分布不一致）的严重影响。现有模型大多关注利用更新的数据，却忽略了可从偏移数据中学习的、可迁移且无时间数据偏移的信息。我们提出关联时间不变性定理，该定理表明：在固定搜索空间下，数据与搜索空间内数据之间的关系随时间保持恒定。基于该原理，我们设计了一种基于检索的推荐系统框架，可利用偏移数据训练无数据偏移的相关性网络，显著提升原推荐模型的预测性能。然而，基于检索的推荐模型在线部署时面临严重的推理时间成本。为解决此问题，我们进一步设计了蒸馏框架，可利用偏移数据将相关性网络的信息蒸馏至参数化模块中。蒸馏后的模型可与原模型在线联合部署，且仅增加极少的推理时间。在多个真实数据集上的大量实验表明，本框架通过利用偏移数据显著提升了原模型的性能。