Interest Entropy: Rethinking Contrastive Learning for Sequential Recommendation with Interest Uncertainty

Sequential Recommendation predicts the next item based on users' past behaviors, but sparse interaction data makes user preferences hard to learn. Recently, contrastive learning has shown promise in this area. It augments data to form positive pairs and maximizing their similarity, allowing the model to learn more generalizable user interests. However, they mainly adopt uniform augmentation and alignment to all sequences, ignoring the challenges arising from their distinct interest structure, namely semantic discrepancy and semantic bias. In this paper, we first study the impact of augmentation on sequence's semantic through Interest Entropy, which measures the diversity and density of interest distribution. Our finding shows only a small fraction of sequences are stable under perturbation. These sequences mainly exhibit low or high entropy, reflecting focused or casual interests. This limits the effectiveness of contrastive learning, which relies on semantically consistent positive pairs. Furthermore, with spectral analysis, we show that positive alignment may cause low-entropy sequences to overlook niche interests, while high-entropy sequences may amplify interest-irrelevant signals, which we term semantic bias. Finally, based on Interest Entropy, we propose IERec, a simple yet effective mutual retrieval augmented contrastive learning method that mitigates the above issues in a unified manner. For each anchor sequence (those with low or high entropy), we retrieve semantically similar sequences with complementary entropy, and concatenate them to form a positive view. Sequences that are easily affected, mainly those with medium entropy, are excluded from augmentation. This approach can avoid harmful semantic discrepancy of positive pairs and reduce the effect of the semantic bias, leading to improved performance. Moreover, using interest entropy to guide contrastive learning can further improve existing CL-based SR methods.