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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 361–370 of 554 papers

TitleStatusHype
Intent-Enhanced Data Augmentation for Sequential Recommendation—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation—0
Invariant representation learning for sequential recommendation—0
Sequential Recommendation via Adaptive Robust Attention with Multi-dimensional Embeddings—0
Item Association Factorization Mixed Markov Chains for Sequential Recommendation—0
JEPA4Rec: Learning Effective Language Representations for Sequential Recommendation via Joint Embedding Predictive Architecture—0
LARES: Latent Reasoning for Sequential Recommendation—0
Sequential Recommendation with Causal Behavior Discovery—0
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
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