Intent Contrastive Learning for Sequential Recommendation

TL;DR

This paper introduces Intent Contrastive Learning (ICL), a novel approach that models latent user intents to enhance sequential recommendation systems through contrastive self-supervised learning, improving accuracy and robustness.

Contribution

The paper proposes a general learning paradigm that incorporates latent intent variables into sequential recommendation models using contrastive SSL within an EM framework, which is a novel approach.

Findings

ICL improves recommendation accuracy on four real-world datasets.

ICL enhances robustness against data sparsity and noise.

Latent intent modeling benefits sequential recommendation performance.

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.