Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders (2210.12316v2)

Abstract: Recently, the generality of natural language text has been leveraged to develop transferable recommender systems. The basic idea is to employ pre-trained LLMs~(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.

• The paper introduces VQ-Rec, a novel two-step method that decouples text embeddings from item representations using discrete vector-quantized codes.
• The paper employs contrastive pre-training with enhanced negatives to optimize cross-domain knowledge transfer.
• The paper demonstrates state-of-the-art performance in inductive tasks, enabling robust recommender systems in dynamic, data-scarce environments.

Analyzing Vector-Quantized Item Representations for Transferable Sequential Recommendation Systems

In the field of recommender systems, effectively transferring knowledge across domains while maintaining robust performance represents a significant technical challenge. The paper "Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders" proposes a novel approach named VQ-Rec, which addresses limitations in current models that primarily rely on pre-trained LLMs (PLMs) for item representation.

Key Contributions

The pivotal contribution of this work lies in the introduction of a two-step item representation scheme, which effectively decouples text semantics from item representations through vector-quantized discrete codes. This approach contrasts with previous methods, which directly transcode item descriptions from PLMs into continuous item embeddings. The key elements of the proposal are:

1. Vector-Quantized Item Representations:
   • This involves first encoding the item text into text embeddings using a fixed PLM, followed by transforming these embeddings into discrete vector-quantized indices using optimized product quantization (OPQ). The strategy effectively reduces the tight semantic binding between item texts and representations, mitigating over-emphasis on text features and improving domain adaptability.
2. Contrastive Pre-Training:
   • The authors design a pre-training regimen using contrastive learning, geared towards optimizing the representational capacity for handling multi-domain data. Enhanced negatives, derived from mixed-domain and semi-synthetic data, play a crucial role in refining the transferability of these recommendations.
3. Cross-Domain Fine-Tuning:
   • Fine-tuning is achieved through permutation-based networks that align new domain-specific codes with pre-trained code-embedding matrices, mitigating the semantic gap that typically challenges cross-domain deployment.

Empirical Analysis

The VQ-Rec approach is empirically validated on multiple datasets encompassing both cross-domain and cross-platform scenarios. The experiments highlight VQ-Rec's superiority in inductive tasks, where it surpasses existing models by efficiently recommending novel items previously unseen in the training data. The empirical results are noteworthy, with VQ-Rec consistently outperforming state-of-the-art methods across various metrics.

Implications and Future Directions

The implications of this research extend to several domains. Practically, VQ-Rec provides a robust framework for deploying recommender systems in environments characterized by rapid domain changes and minimal shared information between training and deployment datasets. Conceptually, this research opens a pathway for further exploration into discrete representation computation, including potential expansions to more sophisticated sequence architectures or hybrid discrete-continuous representation frameworks.

Future research directions include optimizing the discrete representations further and exploring domain adaptation strategies beyond permutation matrices to enhance model robustness. Additionally, scaling this framework to include more complex user interaction patterns and multi-modal data could provide valuable insights.

Overall, "Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders" marks a substantial progression towards domain-agnostic recommendation systems. The decoupling of text and representation, combined with effective transfer learning paradigms, represents a promising advancement in recommender system research. Such innovations promise to bolster the applicability and utility of sequential recommendation systems, particularly in dynamic and data-scarce environments.