Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2026

Abstract

Disentangled recommendation within the Variational Autoencoder (VAE) framework aims to capture multiple user interests. While effective, these VAEs are fundamentally constrained by their reliance on interaction data alone, lacking the rich external semantic knowledge needed to properly structure and separate latent interests. Meanwhile, Large Language Models (LLMs) excel at deriving profound user preference signals from textual data. Prevailing methods for integrating LLMs into recommendation, however, either focus on single-interest modeling or perform a shallow fusion by aligning LLM and VAE representation spaces. Thus, they fail to fundamentally shape the VAE's latent space for multi-interest learning, hindering recommendation performance. To bridge this gap, we propose to fundamentally shift the integration point: instead of aligning representation spaces, we bridge the LLM-generated embedding space directly to the VAE's parameter space. Our framework designs hypernetworks to generate parameters governing interest discovery of a disentangled VAE conditioned on LLM-derived user embeddings. This direct bridge injects rich semantic knowledge into the model's learning foundation, preserving the VAE's power for disentangled representation while dramatically enhancing its modeling capability with informative, LLM-structured priors. Extensive experiments on benchmark datasets show that our method yields significant performance gains, unveiling the untapped potential of using LLMs for disentangled recommendation.

Keywords

Disentangled recommendation, Language-parameter bridging, Variational autoencoders (VAEs)

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

SIGIR '26: Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, July 20-24

First Page

1756

Last Page

1766

ISBN

9798400725999

Identifier

10.1145/3805712.3809537

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3805712.3809537

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