Debiased Contrastive Learning for Sequential Recommendation (2303.11780v1)

Abstract: Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GNNs). However, inference from the highly sparse user behavior data may hinder the representation ability of sequential pattern encoding. To address the label shortage issue, contrastive learning (CL) methods are proposed recently to perform data augmentation in two fashions: (i) randomly corrupting the sequence data (e.g. stochastic masking, reordering); (ii) aligning representations across pre-defined contrastive views. Although effective, we argue that current CL-based methods have limitations in addressing popularity bias and disentangling of user conformity and real interest. In this paper, we propose a new Debiased Contrastive learning paradigm for Recommendation (DCRec) that unifies sequential pattern encoding with global collaborative relation modeling through adaptive conformity-aware augmentation. This solution is designed to tackle the popularity bias issue in recommendation systems. Our debiased contrastive learning framework effectively captures both the patterns of item transitions within sequences and the dependencies between users across sequences. Our experiments on various real-world datasets have demonstrated that DCRec significantly outperforms state-of-the-art baselines, indicating its efficacy for recommendation. To facilitate reproducibility of our results, we make our implementation of DCRec publicly available at: https://github.com/HKUDS/DCRec.

Debiased Contrastive Learning for Sequential Recommendation

The paper "Debiased Contrastive Learning for Sequential Recommendation" introduces an innovative approach to tackling the challenges inherent in sequential recommender systems, including popularity bias in user data and the complexities of user conformity versus genuine interests. These challenges are particularly acute in scenarios where user behavior data is sparse, thereby limiting the effectiveness of conventional neural methods like Transformers and Graph Neural Networks (GNNs) in accurately capturing sequential patterns.

Key Contributions

One of the paper's significant contributions is the introduction of Debiased Contrastive Learning (DCRec). DCRec aims to provide more accurate recommendations by disentangling user conformity from actual interest using a multi-channel conformity weighting network. This network works by providing adaptive conformity-aware augmentation, which distinguishes between real user interests and behaviors influenced by popularity bias.

DCRec integrates sequential pattern encoding with global collaborative relation modeling. Two types of item graphs are used: the item transition graph to capture order-dependent relationships within sequences and the item co-interaction graph to model collaborative signals among users. This dual-graph approach ensures that user intent is modeled both within individual sequences and across user behaviors globally.

Moreover, the paper emphasizes the considerable improvement in recommendation effectiveness achieved by competitively evaluated DCRec on datasets such as Reddit, Beauty, Sports, and MovieLens. The results underscore DCRec's superior performance compared to state-of-the-art models, particularly in scenarios populated by sparse and noisy user interaction data.

Methodology Insights

DCRec leverages the strengths of contrastive learning, which traditionally has shown success in scenarios involving sparse labeling. However, the paper identifies limitations in existing models that fail to address popularity bias—where popular items disproportionately influence user interaction modeling and recommendation quality. By implementing contrastive learning with conformity-aware augmentation, DCRec effectively reduces the skew caused by popularity bias in augmenting sequence data.

In the paper, a multi-channel conformity weighting network is utilized to assess interaction-level conformity with three semantic channels: user-specific conformity influence, consistency with other users, and subgraph isomorphic properties. This multifactorial approach not only evaluates conformity but normalizes it within the dataset to attain a balance in representations of user interests versus conformity-driven actions.

Contrastive learning applied at both user and item dimensions allows DCRec to learn representations that are dynamic and adaptive. Furthermore, conformity and real-interest disentanglement empowers DCRec to mitigate interaction biases that arise from popularity-influenced behavior, leading to enhanced recommendation task performance.

Future Implications

From a practical standpoint, this research offers significant potential for applications in real-life recommendation systems within domains where user interaction data may be highly polarized towards certain popular item trends. By ensuring accurate user modeling, DCRec can help in reducing biases that may lead to less relevant recommendations.

For theoretical advances, the conformity disentanglement strategies presented can be further explored to refine understanding of user behavior in recommender systems—potentially aiding in the development of more personalized and effective recommendation algorithms. Future work could investigate adaptive neural architecture search in conjunction with DCRec's conformity-aware augmentation to streamline model architecture and enhance sequence encoding.

In conclusion, the paper provides a comprehensive paper and novel solution to some of the nuanced issues faced in sequential recommendation tasks, illustrating measurable improvements and providing insights into the implications of popularity bias and conformity within recommendation systems. The released implementation resources and experimental evaluations underline DCRec’s capability and opportunities for further advancements in AI-driven recommendation systems.