AlphaFree: Recommendation Free from Users, IDs, and GNNs
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 t...
| Authors | Minseo Jeon, Junwoo Jung, Daewon Gwak et al. |
|---|---|
| Primary category | cs.IR |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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