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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 401–410 of 554 papers

TitleStatusHype
Sequential Search with Off-Policy Reinforcement Learning—0
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective—0
Similarity-Guided Diffusion for Contrastive Sequential Recommendation—0
Measuring Recency Bias In Sequential Recommendation Systems—0
Transferable Sequential Recommendation via Vector Quantized Meta Learning—0
Memory efficient location recommendation through proximity-aware representation—0
Slow Thinking for Sequential Recommendation—0
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation—0
TRec: Sequential Recommender Based On Latent Item Trend Information—0
SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation—0
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