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Sequential Recommendation

Sequential recommendation is a sophisticated approach to providing personalized suggestions by analyzing users' historical interactions in a sequential manner. Unlike traditional recommendation systems, which consider items in isolation, sequential recommendation takes into account the temporal order of user actions. This method is particularly valuable in domains where the sequence of events matters, such as streaming services, e-commerce platforms, and social media.

Papers

Showing 1120 of 554 papers

TitleStatusHype
Multi-Behavior Generative RecommendationCode2
IDGenRec: LLM-RecSys Alignment with Textual ID LearningCode2
Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space ModelsCode2
MACRec: a Multi-Agent Collaboration Framework for RecommendationCode2
Improving Sequential Recommendations with LLMsCode2
End-to-end Learnable Clustering for Intent Learning in RecommendationCode2
SSLRec: A Self-Supervised Learning Framework for RecommendationCode2
OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender SystemsCode2
Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)Code2
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation AlgorithmsCode2
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