MixRec: Individual and Collective Mixing Empowers Data Augmentation for Recommender Systems

TL;DR

MixRec introduces individual and collective mixing strategies for data augmentation in recommender systems, improving effectiveness and efficiency by reducing manual design and computational complexity.

Contribution

The paper presents a novel dual mixing framework with a unified parameter for data augmentation, enhancing recommender systems without complex manual tuning.

Findings

Outperforms baseline methods on four real-world datasets

Achieves higher recommendation accuracy with simpler, faster augmentation

Demonstrates scalability and effectiveness of the proposed approach

Abstract

The core of the general recommender systems lies in learning high-quality embedding representations of users and items to investigate their positional relations in the feature space. Unfortunately, data sparsity caused by difficult-to-access interaction data severely limits the effectiveness of recommender systems. Faced with such a dilemma, various types of self-supervised learning methods have been introduced into recommender systems in an attempt to alleviate the data sparsity through distribution modeling or data augmentation. However, most data augmentation relies on elaborate manual design, which is not only not universal, but the bloated and redundant augmentation process may significantly slow down model training progress. To tackle these limitations, we propose a novel Dual Mixing-based Recommendation Framework (MixRec) to empower data augmentation as we wish. Specifically, we propose individual mixing and collective mixing, respectively. The former aims to provide a new positive sample that is unique to the target (user or item) and to make the pair-wise recommendation loss benefit from it, while the latter aims to portray a new sample that contains group properties in a batch. The two mentioned mixing mechanisms allow for data augmentation with only one parameter that does not need to be set multiple times and can be done in linear time complexity. Besides, we propose the dual-mixing contrastive learning to maximize the utilization of these new-constructed samples to enhance the consistency between pairs of positive samples. Experimental results on four real-world datasets demonstrate the advantages of MixRec in terms of effectiveness, simplicity, efficiency, and scalability.