Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders

TL;DR

VQ-Rec introduces vector-quantized item representations for transferable sequential recommenders, improving cross-domain transferability by decoupling text features from item representations and employing contrastive pre-training with hard negatives.

Contribution

The paper proposes a novel vector-quantized item representation scheme and a cross-domain fine-tuning method, enhancing transferability and robustness of sequential recommender systems.

Findings

Effective in cross-domain transfer tasks

Improves recommendation accuracy across platforms

Outperforms existing methods on six benchmarks

Abstract

Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained language models~(PLM) to encode item text into item representations. Despite the promising transferability, the binding between item text and item representations might be too tight, leading to potential problems such as over-emphasizing the effect of text features and exaggerating the negative impact of domain gap. To address this issue, this paper proposes VQ-Rec, a novel approach to learning Vector-Quantized item representations for transferable sequential Recommenders. The main novelty of our approach lies in the new item representation scheme: it first maps item text into a vector of discrete indices (called item code), and then employs these indices to lookup the code embedding table for deriving item representations. Such a scheme can be denoted as "text $\Longrightarrow$ code $\Longrightarrow$ representation". Based on this representation scheme, we further propose an enhanced contrastive pre-training approach, using semi-synthetic and mixed-domain code representations as hard negatives. Furthermore, we design a new cross-domain fine-tuning method based on a differentiable permutation-based network. Extensive experiments conducted on six public benchmarks demonstrate the effectiveness of the proposed approach, in both cross-domain and cross-platform settings. Code and pre-trained model are available at: https://github.com/RUCAIBox/VQ-Rec.