Linxyhaha/DEALRec
Data-efficient Fine-tuning for LLM-based Recommendation (SIGIR'24)
This project helps machine learning engineers and researchers to fine-tune large language models (LLMs) for recommendation systems more efficiently. It takes your existing user interaction data and prunes it to select the most informative samples, which are then used to fine-tune an LLM-based recommender model, resulting in a more performant model with less data. This is ideal for those working on product recommendations, content suggestions, or personalized experiences.
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Use this if you are developing recommendation systems with LLMs and need to reduce the amount of data and computational resources required for fine-tuning while maintaining or improving performance.
Not ideal if you are looking for a pre-trained, ready-to-use recommendation system without the need for custom model fine-tuning or if you are not working with LLM-based recommenders.
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Python
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Last pushed
Feb 21, 2025
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