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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 411–420 of 554 papers

TitleStatusHype
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings—0
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers—0
Determinantal Point Process Likelihoods for Sequential RecommendationCode1
Decoupled Side Information Fusion for Sequential RecommendationCode1
Exploiting Session Information in BERT-based Session-aware Sequential RecommendationCode1
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
ELECRec: Training Sequential Recommenders as DiscriminatorsCode1
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-AttentionCode1
Coarse-to-Fine Sparse Sequential Recommendation—0
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