Augmenting Sequential Recommendation with Balanced Relevance and Diversity

TL;DR

This paper introduces BASRec, a novel data augmentation method for sequential recommendation that balances relevance and diversity, significantly improving recommendation performance by generating more effective augmented data.

Contribution

BASRec is a new data augmentation framework that combines single-sequence and cross-sequence augmentation with adaptive reweighting to enhance relevance-diversity balance in sequential recommendation.

Findings

Up to 72.0% improvement on GRU4Rec

Up to 33.8% improvement on SASRec

Better balance of relevance and diversity than existing methods

Abstract

By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on augmenting the original data but rarely explore the issue of imbalanced relevance and diversity for augmented data, leading to semantic drift problems or limited performance improvements. In this paper, we propose a novel Balanced data Augmentation Plugin for Sequential Recommendation (BASRec) to generate data that balance relevance and diversity. BASRec consists of two modules: Single-sequence Augmentation and Cross-sequence Augmentation. The former leverages the randomness of the heuristic operators to generate diverse sequences for a single user, after which the diverse and the original sequences are fused at the representation level to obtain relevance. Further, we devise a reweighting strategy to enable the model to learn the preferences based on the two properties adaptively. The Cross-sequence Augmentation performs nonlinear mixing between different sequence representations from two directions. It produces virtual sequence representations that are diverse enough but retain the vital semantics of the original sequences. These two modules enhance the model to discover fine-grained preferences knowledge from single-user and cross-user perspectives. Extensive experiments verify the effectiveness of BASRec. The average improvement is up to 72.0% on GRU4Rec, 33.8% on SASRec, and 68.5% on FMLP-Rec. We demonstrate that BASRec generates data with a better balance between relevance and diversity than existing methods. The source code is available at https://github.com/KingGugu/BASRec.