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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 501–510 of 554 papers

TitleStatusHype
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention—0
CITIES: Contextual Inference of Tail-Item Embeddings for Sequential Recommendation—0
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation—0
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks—0
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation—0
UPRec: User-Aware Pre-training for Recommender Systems—0
Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels—0
Freudian and Newtonian Recurrent Cell for Sequential Recommendation—0
Offline Adaptive Policy Leaning in Real-World Sequential Recommendation Systems—0
Cold-start Sequential Recommendation via Meta Learner—0
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