Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation

TL;DR

This paper introduces DiQDiff, a diffusion-based sequence recommendation model that enhances user interest understanding through semantic vector quantization and promotes personalized item generation via contrastive discrepancy maximization, outperforming existing methods.

Contribution

The paper proposes a novel guidance method combining semantic vector quantization and contrastive learning to improve personalization and robustness in diffusion-based sequence recommendation.

Findings

DiQDiff outperforms baseline models on four datasets.

Semantic vector quantization enriches user interest understanding.

Contrastive discrepancy maximization enhances personalized item generation.

Abstract

Diffusion models (DMs) have emerged as promising approaches for sequential recommendation due to their strong ability to model data distributions and generate high-quality items. Existing work typically adds noise to the next item and progressively denoises it guided by the user's interaction sequence, generating items that closely align with user interests. However, we identify two key issues in this paradigm. First, the sequences are often heterogeneous in length and content, exhibiting noise due to stochastic user behaviors. Using such sequences as guidance may hinder DMs from accurately understanding user interests. Second, DMs are prone to data bias and tend to generate only the popular items that dominate the training dataset, thus failing to meet the personalized needs of different users. To address these issues, we propose Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation (DiQDiff), which aims to extract robust guidance to understand user interests and generate distinguished items for personalized user interests within DMs. To extract robust guidance, DiQDiff introduces Semantic Vector Quantization (SVQ) to quantize sequences into semantic vectors (e.g., collaborative signals and category interests) using a codebook, which can enrich the guidance to better understand user interests. To generate distinguished items, DiQDiff personalizes the generation through Contrastive Discrepancy Maximization (CDM), which maximizes the distance between denoising trajectories using contrastive loss to prevent biased generation for different users. Extensive experiments are conducted to compare DiQDiff with multiple baseline models across four widely-used datasets. The superior recommendation performance of DiQDiff against leading approaches demonstrates its effectiveness in sequential recommendation tasks.