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Models that generate item identifiers as a sequence instead of scoring and ranking a list.
Distinct from Recommendation Models: Distinct from traditional recommendation models that predict ratings or rank existing lists by generating new item sequences.
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Awesome Generative GitHub Repositories
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- datawhalechina/fun-rec
datawhalechina/fun-rec
7,177Auf GitHub ansehenfun-rec is a learning guide and framework for building personalized recommendation systems, covering everything from deep learning ranking to generative recommendation paradigms. It provides instructional content on constructing industrial-grade architectures that span offline data processing and real-time online serving. The project distinguishes itself by focusing on generative recommendation, treating the suggestion process as a sequence-to-sequence task using large language models and transformer models to generate item identifiers rather than traditional ranking lists. It also emphasizes
Implements generative recommendation paradigms using LLMs and diffusion models to generate item suggestions directly.
Pythonalgorithm-engineeringdeep-learninginterview-questions
