glb400/Toy-RecLM
A toy large model for recommender system based on LLaMA2/SASRec/Meta's generative recommenders. Besides, note and experiments of official implementation for Meta's generative recommenders.
This project helps e-commerce and content platforms predict what products or content a user will engage with next, based on their past interactions. You provide sequences of user behavior (like items viewed or purchased), and it generates recommendations tailored to that user's likely future interests. This is for data scientists or machine learning engineers building and evaluating next-item recommendation systems.
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Use this if you are developing or experimenting with advanced generative AI models for sequential recommendation, particularly those based on large language model architectures.
Not ideal if you need a plug-and-play solution for a production recommendation system without deep technical engagement, or if you prefer traditional collaborative filtering methods.
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69
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6
Language
Python
License
MIT
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Last pushed
Apr 25, 2024
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