Quality-Aware Collaborative Multi-Positive Contrastive Learning for Sequential Recommendation

TL;DR

This paper introduces a novel contrastive learning framework for sequential recommendation that adaptively weights augmented views based on their quality, improving view diversity and semantic consistency.

Contribution

It proposes a learnable collaborative augmentation method and a quality-aware weighting mechanism to enhance contrastive learning effectiveness in sequential recommendation.

Findings

Outperforms state-of-the-art CL-based recommendation methods on three datasets.

Enhances view diversity and semantic consistency through collaborative augmentation.

Adaptive weighting improves the contribution of high-quality views.

Abstract

The effectiveness of contrastive learning in sequential recommendation hinges on the construction of contrastive views, which ideally should be both semantically consistent and diverse. However, most existing CL-based methods rely on heuristic augmentations that are prone to removing crucial items or disrupting transition patterns, leading to semantic drift. While a few studies have explored learnable augmentations to improve view quality, they often suffer from limited diversity and still necessitate heuristic aids. Furthermore, the quality differences across views are rarely modeled explicitly and adaptively, aggravating the false-positive issue. To address these issues, we propose Quality-aware Collaborative Multi-Positive Contrastive Learning for sequential recommendation. First, we introduce a learnable collaborative sequence augmentation module that generates two augmented views under two complementary collaborative contexts, one based on same-target sequences and the other on similar sequences, thereby enhancing view diversity while preserving intent consistency.Second, we design a quality-aware mechanism, tightly integrated into the model representations, which estimates each view' s quality from the confidence of its augmentation operations and assigns adaptive weights to ensure that high-confidence views contribute more supervision while low-confidence ones contribute less.Extensive experiments on three real-world datasets demonstrate that QCMP-CL outperforms state-of-the-art CL-based sequential recommendation baselines.