Multi-level Contrastive Learning Framework for Sequential Recommendation

TL;DR

This paper introduces a multi-level contrastive learning framework for sequential recommendation that captures complex collaborative and co-action information at interest and feature levels, improving recommendation accuracy.

Contribution

It proposes a novel multi-level contrastive learning framework (MCLSR) that models user-item relations at interest and feature levels for better sequential recommendation.

Findings

MCLSR outperforms state-of-the-art methods on four datasets.

The interest-level contrastive mechanism captures collaborative and sequential patterns.

The feature-level contrastive mechanism models co-action relations effectively.

Abstract

Sequential recommendation (SR) aims to predict the subsequent behaviors of users by understanding their successive historical behaviors. Recently, some methods for SR are devoted to alleviating the data sparsity problem (i.e., limited supervised signals for training), which take account of contrastive learning to incorporate self-supervised signals into SR. Despite their achievements, it is far from enough to learn informative user/item embeddings due to the inadequacy modeling of complex collaborative information and co-action information, such as user-item relation, user-user relation, and item-item relation. In this paper, we study the problem of SR and propose a novel multi-level contrastive learning framework for sequential recommendation, named MCLSR. Different from the previous contrastive learning-based methods for SR, MCLSR learns the representations of users and items through a cross-view contrastive learning paradigm from four specific views at two different levels (i.e., interest- and feature-level). Specifically, the interest-level contrastive mechanism jointly learns the collaborative information with the sequential transition patterns, and the feature-level contrastive mechanism re-observes the relation between users and items via capturing the co-action information (i.e., co-occurrence). Extensive experiments on four real-world datasets show that the proposed MCLSR outperforms the state-of-the-art methods consistently.