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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 331340 of 554 papers

TitleStatusHype
CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation0
Are We Really Achieving Better Beyond-Accuracy Performance in Next Basket Recommendation?0
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement0
TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal ContentCode0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
Beyond the Sequence: Statistics-Driven Pre-training for Stabilizing Sequential Recommendation Model0
RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation0
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