  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.
