Multi-Intention Oriented Contrastive Learning for Sequential Recommendation

Abstract

Sequential recommendation aims to capture users' dynamic preferences, in which data sparsity is a key problem. Most contrastive learning models leverage data augmentation to address this problem, but they amplify noises in original sequences. Contrastive learning has the assumption that two views (positive pairs) obtained from the same user behavior sequence must be similar. However, noises typically disturb the user's main intention, which results in the dissimilarity of two views.