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Next basket recommendation aims to infer a set of items that a user will purchase at the next visit by considering a sequence of baskets he/she has purchased previously. This task has drawn increasing attention from both the academic and industrial communities. The existing solutions mainly focus on sequential modeling over their historical interactions. However, due to the diversity and randomness of users' behaviors, not all these baskets are relevant to help identify the user's next move. It is necessary to denoise the baskets and extract credibly relevant items to enhance recommendation performance. Unfortunately, this dimension is usually overlooked in the current literature.

To this end, in this paper, we propose a Contrastive Learning Model~(named CLEA) to automatically extract items relevant to the target item for next basket recommendation. Specifically, empowered by Gumbel Softmax, we devise a …