Large Language Models-Enhanced Semantic Diffusion for User-Centric Recommendation
Year: 2026
Type: conference-paper
Abstract: Recently, knowledge graphs have been utilised in recommendation systems to improve accuracy by integrating item-side auxiliary information. However, structural user-side knowledge is difficult to construct and integrate due to inherent scarcity and improper granularity. This paper introduces a graph contrastive learning with Semantic transitions-Enhanced DIffusion architecture based on Large Language Models (LLMs) for user-side knowledge-aware Recommendation (SEDIRec). Specifically, our SEDIRec first leverages LLMs to infer user interests from historical behaviors, integrating this user-side information with item-side and collaborative data to construct main views. Then, two contrastive views are generated using diffusion models with semantic transitions: one at the user-side level and the other at the item-side level. For both contrastive views, we integrate user-side or item-side information with collaborative data to generate a user-item graph. Subsequently, each user-item graph is transformed into collaborative data spaces via diffusion models for generating contrastive views. This procedure not only enhances the alignment between user/item-side information and the semantic spaces of collaborative data but also effectively eliminates noise. Extensive experiments on three datasets reveal the superiority of SEDIRec, especially for users with sparse interactions.
Source: ACM Web Conference (WWW)
Institutions: Ningxia University, University of Luxembourg
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G8544646609, G8783486029, G8938456724
OpenAlex ID: W7154715667
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