RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations
This work addresses personalization problems in consumer technologies like streaming and shopping, offering an incremental enhancement by combining LLMs with graph-based methods for better recommendation accuracy.
The paper tackled the challenge of personalized recommendations by integrating a frozen large language model (LLM) with a graph neural network (LightGCN) to handle sparse interactions and textual signals, achieving state-of-the-art results with improvements up to +4.53% in Recall@40 and +4.01% in NDCG@40 on datasets like Yelp and Amazon-Electronics.
Personalization is a core capability across consumer technologies, streaming, shopping, wearables, and voice, yet it remains challenged by sparse interactions, fast content churn, and heterogeneous textual signals. We present RecMind, an LLM-enhanced graph recommender that treats the language model as a preference prior rather than a monolithic ranker. A frozen LLM equipped with lightweight adapters produces text-conditioned user/item embeddings from titles, attributes, and reviews; a LightGCN backbone learns collaborative embeddings from the user-item graph. We align the two views with a symmetric contrastive objective and fuse them via intra-layer gating, allowing language to dominate in cold/long-tail regimes and graph structure to stabilize rankings elsewhere. On Yelp and Amazon-Electronics, RecMind attains the best results on all eight reported metrics, with relative improvements up to +4.53\% (Recall@40) and +4.01\% (NDCG@40) over strong baselines. Ablations confirm both the necessity of cross-view alignment and the advantage of gating over late fusion and LLM-only variants.