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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

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Showing 201–210 of 554 papers

TitleStatusHype
RecGPT: Generative Personalized Prompts for Sequential Recommendation via ChatGPT Training Paradigm—0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTCode1
Sequential Recommendation with Latent Relations based on Large Language ModelCode1
IDGenRec: LLM-RecSys Alignment with Textual ID LearningCode2
END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation—0
An Empirical Study of Training ID-Agnostic Multi-modal Sequential RecommendersCode1
Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling—0
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential RecommendationCode1
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential RecommendationCode0
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