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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 281–290 of 554 papers

TitleStatusHype
DiPS: Differentiable Policy for Sketching in Recommender Systems—0
Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation—0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation—0
Diversity-aware Dual-promotion Poisoning Attack on Sequential Recommendation—0
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks—0
DRDT: Dynamic Reflection with Divergent Thinking for LLM-based Sequential Recommendation—0
Rethinking Large Language Model Architectures for Sequential Recommendations—0
Dual Conditional Diffusion Models for Sequential Recommendation—0
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention—0
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