AdaRec: LLMs that explain and adapt your recommendations

Meet AdaRec, a new way to build personalized recommendations with large language models that works even when you have very little data.

Instead of hand-crafted features, AdaRec uses narrative profiling—it turns a person’s interactions into plain-language mini-stories the model can understand and explain.

• Peer patterns (horizontal alignment): learns from similar users’ behavior.
• Why it fits (vertical attribution): highlights the key reasons behind a user’s preferences.

Across real e‑commerce datasets, AdaRec beat traditional ML and LLM baselines by up to 8% in few-shot tests. In zero-shot settings, it surpassed expert-designed profiles by up to 19%, making it especially strong for long‑tail items with minimal interaction data. Lightweight fine‑tuning on AdaRec’s own synthetic data matched fully fine‑tuned models—saving time and compute.

Fewer labels, clearer reasoning, faster adaptation.

Paper by Meiyun Wang and Charin Polpanumas. Read more: http://arxiv.org/abs/2511.07166v1

Paper: http://arxiv.org/abs/2511.07166v1

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