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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 481–490 of 554 papers

TitleStatusHype
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
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training TransformerCode1
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationCode1
Dynamic Graph Neural Networks for Sequential RecommendationCode1
Adversarial and Contrastive Variational Autoencoder for Sequential RecommendationCode1
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
Dynamic Memory based Attention Network for Sequential RecommendationCode1
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