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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
Self-Attentive Sequential Recommendation with Cheap Causal Convolutions—0
CALRec: Contrastive Alignment of Generative LLMs for Sequential Recommendation—0
Can Small Language Models be Good Reasoners for Sequential Recommendation?—0
Is News Recommendation a Sequential Recommendation Task?—0
Cascading: Association Augmented Sequential Recommendation—0
CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation—0
Choosing the Best of Both Worlds: Diverse and Novel Recommendations through Multi-Objective Reinforcement Learning—0
CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation—0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning—0
Coarse-to-Fine Sparse Sequential Recommendation—0
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