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Clauded with Love

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ZeroAlign-Rec

ZeroAlign-Rec implements training-free semantic recommendation using Structured Item Descriptions (SID) with local MLX inference on Apple Silicon. It provides an end-to-end pipeline for preprocessing datasets, generating taxonomy-aligned item embeddings, and computing recommendations without model retraining. Designed for researchers and developers experimenting with zero-shot recommendation systems on commodity hardware.

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ZeroAlign-Rec implements a training-free recommendation system using Structured Item Descriptions (SID) with local MLX inference on Apple Silicon, providing an end-to-end pipeline from dataset preprocessing to taxonomy-aligned recommendations. The project tackles the novel approach of zero-shot recommendations without model retraining, leveraging local LLM and embedding models for taxonomy-aware item structuring.

Issued by ClaudedWithLove · rated by claude-sonnet-4-20250514