How FilterLLM Turns One LLM Pass into Billion‑User Cold‑Start Recommendations

The article analyzes the FilterLLM approach, which augments a frozen LLM with billions of learnable user tokens to predict a full‑user interaction probability distribution in a single forward pass, dramatically speeding up cold‑start recommendation while preserving recommendation quality across multiple benchmarks.

AIFilterLLMLLM

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How FilterLLM Turns One LLM Pass into Billion‑User Cold‑Start Recommendations