Continuous Tokenization Boosts LLM-Based Recommender Systems, Study Finds

Global: Continuous Tokenization Boosts LLM-Based Recommender Systems, Study Finds

Researchers have unveiled ContRec, a novel framework that integrates continuous tokens into large language model (LLM) driven recommender systems, aiming to overcome the performance gaps caused by traditional discrete tokenization. The approach, detailed in a recent arXiv preprint, combines a sigma‑VAE tokenizer with a dispersive diffusion module and reports consistent superiority over existing state‑of‑the‑art methods across four benchmark datasets.

Background

Current LLM‑based recommender systems typically rely on vector‑quantized tokenizers to map user and item information into discrete token spaces. While this aligns with the inherently discrete operation of language models, the quantization process often introduces lossy representations and hampers gradient flow because of the non‑differentiable argmin step used in standard vector quantization.

Continuous Tokenizer

ContRec addresses these limitations through a sigma‑VAE tokenizer that encodes users and items as continuous vectors. Trained under a variational auto‑encoder objective, the tokenizer incorporates three techniques designed to prevent representation collapse, thereby preserving richer semantic detail during encoding.

Diffusion‑Based Preference Modeling

The framework’s second pillar, a dispersive diffusion module, captures implicit user preferences by conditioning on previously generated tokens from the LLM backbone. A novel dispersive loss guides a conditional diffusion process, enabling the generation of high‑quality preference signals via next‑token diffusion.

Integration and Retrieval

ContRec leverages both the textual reasoning output of the LLM and the latent diffusion representations to perform top‑K item retrieval. This dual‑source strategy is intended to provide more comprehensive recommendation results than approaches that rely on a single modality.

Experimental Validation

Extensive experiments on four publicly available datasets demonstrate that ContRec consistently outperforms traditional recommender baselines as well as the latest LLM‑based systems. Metrics reported in the study indicate notable gains in accuracy and relevance, underscoring the potential of continuous tokenization combined with generative diffusion modeling.

Implications

The findings suggest that moving beyond discrete token spaces could open new avenues for improving recommendation quality in AI‑driven platforms. Researchers anticipate that further refinement of continuous tokenizers and diffusion‑based preference models may extend these benefits to broader domains within personalized content delivery.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.