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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 251–260 of 554 papers

TitleStatusHype
Improve Temporal Awareness of LLMs for Sequential Recommendation—0
Conditional Denoising Diffusion for Sequential Recommendation—0
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation—0
Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation—0
Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs—0
A Survey on Multi-Behavior Sequential Recommendation—0
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation—0
Gumble Softmax For User Behavior Modeling—0
Learning to Augment for Casual User Recommendation—0
Learning to Structure Long-term Dependence for Sequential Recommendation—0
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