Linxyhaha/TransRec
Bridging Items and Language: A Transition Paradigm for Large Language Model-Based Recommendation (KDD'24)
This project helps e-commerce managers or product strategists develop more effective recommendation systems. It takes existing product data, user interactions, and textual descriptions, then processes them through large language models to generate improved product recommendations. The output is a list of recommended items, allowing for more precise and personalized suggestions to customers.
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Use this if you need to build or enhance a product recommendation engine that leverages advanced language models to understand and connect product features with user preferences.
Not ideal if you are looking for a simple, off-the-shelf recommendation solution without the need for deep customization using large language models or extensive data preprocessing.
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C++
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Last pushed
Aug 01, 2024
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