This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.
Dynllm: when large language models meet dynamic graph recommendation
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G2Rec unifies holistic graph-based user co-engagement modeling with semantic tokenization for scalable generative recommendation without ground-truth user interests.
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A Survey on Generative Recommendation: Data, Model, and Tasks
This survey organizes generative recommendation into data, model, and task dimensions, identifying five advantages including world knowledge integration and creative generation while noting challenges in benchmarks and efficiency.
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Structuring and Tokenizing Distributed User Interest Context for Generative Recommendation
G2Rec unifies holistic graph-based user co-engagement modeling with semantic tokenization for scalable generative recommendation without ground-truth user interests.