Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendation

TL;DR

This paper introduces iDreamRec, a diffusion-based recommendation model that uses detailed text descriptions and advanced text embeddings to improve item modeling and incorporate control signals, enhancing recommendation quality.

Contribution

The paper proposes a novel method leveraging item text descriptions and text embeddings to better model data distribution and incorporate control signals in diffusion-based recommendations.

Findings

iDreamRec outperforms existing diffusion recommenders on four datasets.

It effectively incorporates intention instructions for more precise recommendations.

The approach improves modeling of oracle items using detailed text descriptions.

Abstract

Recent advancements in generative recommendation systems, particularly in the realm of sequential recommendation tasks, have shown promise in enhancing generalization to new items. Among these approaches, diffusion-based generative recommendation has emerged as an effective tool, leveraging its ability to capture data distributions and generate high-quality samples. Despite effectiveness, two primary challenges have been identified: 1) the lack of consistent modeling of data distribution for oracle items; and 2) the difficulty in scaling to more informative control signals beyond historical interactions. These issues stem from the uninformative nature of ID embeddings, which necessitate random initialization and limit the incorporation of additional control signals. To address these limitations, we propose iDreamRec to involve more concrete prior knowledge to establish item embeddings, particularly through detailed item text descriptions and advanced Text Embedding Models (TEM). More importantly, by converting item descriptions into embeddings aligned with TEM, we enable the integration of intention instructions as control signals to guide the generation of oracle items. Experimental results on four datasets demonstrate that iDreamRec not only outperforms existing diffusion-based generative recommenders but also facilitates the incorporation of intention instructions for more precise and effective recommendation generation.