  Learning the user-item relevance hidden in implicit feedback data plays an
important role in modern recommender systems. Neural sequential recommendation
models, which formulates learning the user-item relevance as a sequential
classification problem to distinguish items in future behaviors from others
based on the user's historical behaviors, have attracted a lot of interest in
both industry and academic due to their substantial practical value. Though
achieving many practical successes, we argue that the intrinsic {\bf
incompleteness} and {\bf inaccuracy} of user behaviors in implicit feedback
data is ignored and conduct preliminary experiments for supporting our claims.
Motivated by the observation that model-free methods like behavioral
retargeting (BR) and item-based collaborative filtering (ItemCF) hit different
parts of the user-item relevance compared to neural sequential recommendation
models, we propose a novel model-agnostic training approach called WSLRec,
which adopts a three-stage framework: pre-training, top-$k$ mining, and
fine-tuning. WSLRec resolves the incompleteness problem by pre-training models
on extra weak supervisions from model-free methods like BR and ItemCF, while
resolves the inaccuracy problem by leveraging the top-$k$ mining to screen out
reliable user-item relevance from weak supervisions for fine-tuning.
Experiments on two benchmark datasets and online A/B tests verify the
rationality of our claims and demonstrate the effectiveness of WSLRec.
