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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 371–380 of 554 papers

TitleStatusHype
Laser: Parameter-Efficient LLM Bi-Tuning for Sequential Recommendation with Collaborative Information—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Learnable Sequence Augmenter for Triplet Contrastive Learning in Sequential Recommendation—0
Sequential Recommendation with Diffusion Models—0
Learning Graph ODE for Continuous-Time Sequential Recommendation—0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation—0
Learning Post-Hoc Causal Explanations for Recommendation—0
Gumble Softmax For User Behavior Modeling—0
Learning to Augment for Casual User Recommendation—0
Learning to Learn a Cold-start Sequential Recommender—0
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