Intent Contrastive Learning for Sequential Recommendation (2202.02519v1)

Abstract: Users' interactions with items are driven by various intents (e.g., preparing for holiday gifts, shopping for fishing equipment, etc.).However, users' underlying intents are often unobserved/latent, making it challenging to leverage such latent intents forSequentialrecommendation(SR). To investigate the benefits of latent intents and leverage them effectively for recommendation, we proposeIntentContrastiveLearning(ICL), a general learning paradigm that leverages a latent intent variable into SR. The core idea is to learn users' intent distribution functions from unlabeled user behavior sequences and optimize SR models with contrastive self-supervised learning (SSL) by considering the learned intents to improve recommendation. Specifically, we introduce a latent variable to represent users' intents and learn the distribution function of the latent variable via clustering. We propose to leverage the learned intents into SR models via contrastive SSL, which maximizes the agreement between a view of sequence and its corresponding intent. The training is alternated between intent representation learning and the SR model optimization steps within the generalized expectation-maximization (EM) framework. Fusing user intent information into SR also improves model robustness. Experiments conducted on four real-world datasets demonstrate the superiority of the proposed learning paradigm, which improves performance, and robustness against data sparsity and noisy interaction issues.

• The paper introduces a novel intent contrastive learning framework that leverages latent user intents to enhance sequential recommendation models.
• It combines contrastive self-supervised learning with an expectation-maximization framework to refine user intent representations.
• Empirical results on diverse datasets show significant improvements in HR and NDCG, demonstrating enhanced personalization and robustness.

Intent Contrastive Learning for Sequential Recommendation

The paper, "Intent Contrastive Learning for Sequential Recommendation," addresses the challenge of leveraging users' latent intents for improving sequential recommendation (SR) models in recommender systems. Users' interactions with items in recommender systems are often driven by latent intents, such as shopping for a particular event or acquiring specialized equipment. These latent intents are typically unobserved, making it difficult for recommendation algorithms to effectively utilize these signals. The authors propose a novel approach called Intent Contrastive Learning (ICL) to better leverage these latent user intents, presenting both a theoretical and practical framework for enhancing SR models.

Core Contributions

1. Latent Intent Modeling: The paper introduces a latent intent variable to represent user intents in SR models. By employing clustering methods, the method learns a distribution function over these variables, thereby capturing the underlying intent from sequences of user interactions.
2. Contrastive Self-Supervised Learning (SSL): ICL leverages contrastive SSL, a popular method in recent machine learning literature, to fuse these learned intents into SR models. The method accentuates the agreement between a sequence's view and its corresponding latent intent, effectively maximizing mutual information and strengthening the model's ability to predict future interactions.
3. Expectation-Maximization (EM) Framework: The training paradigm operates within an expectation-maximization framework that alternates between learning users' intent distributions and optimizing the SR model. This cyclical process ensures convergence and robustness in learning.

Experimental Results

The authors conduct extensive experiments on four diverse real-world datasets, demonstrating the efficacy of the proposed ICL approach. The empirical results show significant performance improvements over other state-of-the-art methods across various metrics such as HR@k and NDCG@k. Specifically, the improvements range from 7.47% to 33.33% in hit ratio (HR) and normalized discounted cumulative gain (NDCG), underscoring the effectiveness of integrating latent user intents into the recommendation models.

Robustness and Scalability

The paper includes robustness experiments against long-tail user interaction scenarios and noisy data, showing that ICLRec (the term used for the ICL-enhanced SR model) consistently outperforms comparative models, including SASRec and CL4SRec. This demonstrates the potential of ICL to alleviate typical challenges in recommendation systems such as user cold-start and data sparsity issues. The robustness analysis further confirms the utility of incorporating latent intent representations.

Practical and Theoretical Implications

Practically, the ICL framework could be implemented to enhance existing SR systems' capabilities to deliver more personalized content, thanks to its ability to discern and incorporate latent intent information. Theoretically, this paper contributes to the growing evidence supporting the integration of contrastive learning paradigms into recommendation systems, especially in cases where labeled intent data is unavailable. Moreover, by framing the problem within an EM framework, the work provides a solid theoretical foundation for convergence, which is crucial for real-world applicability.

Future Directions

The paper paves the way for several future research avenues. One potential area for development is exploring more sophisticated methods for representing and clustering latent intents, possibly leveraging neural network architectures for representation learning beyond simple clustering methods. Additionally, integrating side information or auxiliary data sources might further refine the intent estimation process, improving performance even in sparse data scenarios.

In summary, this paper presents a compelling approach to incorporating latent user intents into sequential recommendation models using intent contrastive learning. Its integration of contrastive SSL within an EM framework addresses several longstanding challenges in recommender systems, making it a notable contribution to the field of AI-driven personalization and recommendation.