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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 321–330 of 554 papers

TitleStatusHype
Self-Supervised Reinforcement Learning for Recommender Systems—0
Freudian and Newtonian Recurrent Cell for Sequential Recommendation—0
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation—0
Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendation—0
Generating Negative Samples for Sequential Recommendation—0
Generative Diffusion Models for Sequential Recommendations—0
Generative Sequential Recommendation with GPTRec—0
GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-training—0
GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation—0
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems—0
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