LLM-Grounded Diffusion for Cross-Domain Recommendation

摘要

Cross-Domain Recommendation (CDR) leverages auxiliary information from different domains to enhance the target domain. However, most CDR models capture coarse-grained user preferences, as they focus on learning fixed domain-invariant embeddings that overlook semantics in user-generated texts. In this paper, we propose a novel framework, LLM-grounded diffusion for CDR (LLDCDR), which integrates LLMs and diffusion models to model fine-grained user preferences by learning multi-aspect domain-invariant representations. First, we leverage the advanced understanding abilities of LLMs to capture multi-faceted common semantics influencing user preferences across different domains. Second, we present an LLM-grounded conditional diffusion to reduce noises of domain-invariant representations by performing multi-step noise diffusion and denoising. To further disentangle multi-aspect semantics, we conceptualize domain-invariant representation learning as a conditional diffusion process, guiding the diffusion using distinct semantic aspects derived from LLM. Finally, we encapsulate LLDCDR into a plug-in framework by modularizing the above strategies. This allows LLDCDR to be integrated into any existing CDR models.