[2603.02653] AlphaFree: Recommendation Free from Users, IDs, and GNNs

Computer Science > Information Retrieval arXiv:2603.02653 (cs) [Submitted on 3 Mar 2026] Title:AlphaFree: Recommendation Free from Users, IDs, and GNNs Authors:Minseo Jeon, Junwoo Jung, Daewon Gwak, Jinhong Jung View a PDF of the paper titled AlphaFree: Recommendation Free from Users, IDs, and GNNs, by Minseo Jeon and 3 other authors View PDF Abstract:Can we design effective recommender systems free from users, IDs, and GNNs? Recommender systems are central to personalized content delivery across domains, with top-K item recommendation being a fundamental task to retrieve the most relevant items from historical interactions. Existing methods rely on entrenched design conventions, often adopted without reconsideration, such as storing per-user embeddings (user-dependent), initializing features from raw IDs (ID-dependent), and employing graph neural networks (GNN-dependent). These dependencies incur several limitations, including high memory costs, cold-start and over-smoothing issues, and poor generalization to unseen interactions. In this work, we propose AlphaFree, a novel recommendation method free from users, IDs, and GNNs. Our main ideas are to infer preferences on-the-fly without user embeddings (user-free), replace raw IDs with language representations (LRs) from pre-trained language models (ID-free), and capture collaborative signals through augmentation with similar items and contrastive learning, without GNNs (GNN-free). Extensive experiments on various real-world...