  Negative sampling methods are vital in implicit recommendation models as they
allow us to obtain negative instances from massive unlabeled data. Most
existing approaches focus on sampling hard negative samples in various ways.
These studies are orthogonal to the recommendation model and implicit datasets.
However, such an idea contradicts the common belief in AutoML that the model
and dataset should be matched. Empirical experiments suggest that the
best-performing negative sampler depends on the implicit dataset and the
specific recommendation model. Hence, we propose a hypothesis that the negative
sampler should align with the capacity of the recommendation models as well as
the statistics of the datasets to achieve optimal performance. A mismatch
between these three would result in sub-optimal outcomes. An intuitive idea to
address the mismatch problem is to exhaustively select the best-performing
negative sampler given the model and dataset. However, such an approach is
computationally expensive and time-consuming, leaving the problem unsolved. In
this work, we propose the AutoSample framework that adaptively selects the
best-performing negative sampler among candidates. Specifically, we propose a
loss-to-instance approximation to transform the negative sampler search task
into the learning task over a weighted sum, enabling end-to-end training of the
model. We also designed an adaptive search algorithm to extensively and
efficiently explore the search space. A specific initialization approach is
also obtained to better utilize the obtained model parameters during the search
stage, which is similar to curriculum learning and leads to better performance
and less computation resource consumption. We evaluate the proposed framework
on four benchmarks over three models. Extensive experiments demonstrate the
effectiveness and efficiency of our proposed framework.
