RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations

• URL: http://arxiv.org/abs/2509.06286v1
• Date: Mon, 08 Sep 2025 02:15:55 GMT
• Title: RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations
• Authors: Chang Xue, Youwei Lu, Chen Yang, Jinming Xing,
• Abstract summary: Personalization is a core capability across consumer technologies, streaming, shopping, wearables, and voice.<n>We present RecMind, an LLM-enhanced graph recommender that treats the language model as a preference prior to a monolithic ranker.<n>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.
• Score: 1.9556834179471867
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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