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README.md

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 # recICL
 
-**recICL is a next-item recommender**. We trained this checkpoint ourselves, from scratch, synthetic-data prior, and packaged it for inference.  It takes the text embeddings of a user's recent items plus a few other users' sequences as context. From these it ranks a whole catalog in one forward pass, with no training on that catalog. It has 170M parameters and uses 1536-d item embeddings from `gte-Qwen2-1.5B-instruct`.
+**recICL is a In Context Learning recommender**. We trained this checkpoint ourselves, from scratch, synthetic-data prior, and packaged it for inference.  It takes the text embeddings of a user's recent items plus a few other users' sequences as context. From these it ranks a whole catalog in one forward pass, with no training on that catalog. It has 170M parameters and uses 1536-d item embeddings from `gte-Qwen2-1.5B-instruct`.
 
 **Status: v0 research checkpoint.** On our 7 Amazon test sets it is clearly better than a mean-of-history embedding baseline. See [Evaluation](#evaluation) and [Limitations](#limitations).