  Micro-video recommender systems suffer from the ubiquitous noises in users'
behaviors, which might render the learned user representation indiscriminating,
and lead to trivial recommendations (e.g., popular items) or even weird ones
that are far beyond users' interests. Contrastive learning is an emergent
technique for learning discriminating representations with random data
augmentations. However, due to neglecting the noises in user behaviors and
treating all augmented samples equally, the existing contrastive learning
framework is insufficient for learning discriminating user representations in
recommendation. To bridge this research gap, we propose the Contrast over
Contrastive Learning framework for training recommender models, named CCL4Rec,
which models the nuances of different augmented views by further contrasting
augmented positives/negatives with adaptive pulling/pushing strengths, i.e.,
the contrast over (vanilla) contrastive learning. To accommodate these
contrasts, we devise the hardness-aware augmentations that track the importance
of behaviors being replaced in the query user and the relatedness of
substitutes, and thus determining the quality of augmented positives/negatives.
The hardness-aware augmentation also permits controllable contrastive learning,
leading to performance gains and robust training. In this way, CCL4Rec captures
the nuances of historical behaviors for a given user, which explicitly shields
off the learned user representation from the effects of noisy behaviors. We
conduct extensive experiments on two micro-video recommendation benchmarks,
which demonstrate that CCL4Rec with far less model parameters could achieve
comparable performance to existing state-of-the-art method, and improve the
training/inference speed by several orders of magnitude.
