UniRec: Unified Space Learning for Generalizable Sequential Recommendation

Abstract

Sequential recommendation aims to predict users’ next items of interest based on their historical interactions. Recently, Large Language Models (LLMs) have shown strong potential in this field due to their powerful semantic understanding. However, existing methods face two core challenges: First, LLMs may generate non-existent recommendations due to hallucinations; Second, when utilizing LLM-generated embeddings for transfer to downstream recommendation tasks in different domains, the dimensional and structural differences across spaces exacerbate space misalignment. To address these challenges, we propose UniRec, a unified space learning framework that achieves deep alignment through a hierarchical space transformation path: language space → collaborative space → real space. This framework employs a two-stage learning mechanism: The first stage guides LLMs to align semantic and collaborative spaces through collaborative instruction fine-tuning, introduces a real-space constrained generation mechanism to reduce hallucination problems, and is optimized for large-scale applications; The second stage extracts core semantic principal components through singular value decomposition and freezes the representations, combines with gating networks to adaptively fuse semantic and domain-specific collaborative signals, achieving efficient cross-domain transfer. Experiments show that UniRec achieves over 27% average improvement on in-domain datasets and 24% on out-of-domain datasets, while maintaining low memory overhead.