{"ID":2873248,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06286","arxiv_id":"2509.06286","title":"RecMind: LLM-Enhanced Graph Neural Networks for Personalized Consumer Recommendations","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.","short_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 prio...","url_abs":"https://arxiv.org/abs/2509.06286","url_pdf":"https://arxiv.org/pdf/2509.06286v1","authors":"[\"Chang Xue\",\"Youwei Lu\",\"Chen Yang\",\"Jinming Xing\"]","published":"2025-09-08T02:15:55Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"Graph Neural Network\",\"Large Language Model\",\"Language Model\"]","has_code":false}