  Recommender retrievers aim to rapidly retrieve a fraction of items from the
entire item corpus when a user query requests, with the representative
two-tower model trained with the log softmax loss. For efficiently training
recommender retrievers on modern hardwares, inbatch sampling, where the items
in the mini-batch are shared as negatives to estimate the softmax function, has
attained growing interest. However, existing inbatch sampling based strategies
just correct the sampling bias of inbatch items with item frequency, being
unable to distinguish the user queries within the mini-batch and still
incurring significant bias from the softmax. In this paper, we propose a
Cache-Augmented Inbatch Importance Resampling (XIR) for training recommender
retrievers, which not only offers different negatives to user queries with
inbatch items, but also adaptively achieves a more accurate estimation of the
softmax distribution. Specifically, XIR resamples items for the given
mini-batch training pairs based on certain probabilities, where a cache with
more frequently sampled items is adopted to augment the candidate item set,
with the purpose of reusing the historical informative samples. XIR enables to
sample query-dependent negatives based on inbatch items and to capture dynamic
changes of model training, which leads to a better approximation of the softmax
and further contributes to better convergence. Finally, we conduct experiments
to validate the superior performance of the proposed XIR compared with
competitive approaches.
