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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 401–425 of 554 papers

TitleStatusHype
Multi-Behavior Sequential Recommendation with Temporal Graph TransformerCode1
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual ActorCode1
Enhancing Sequential Recommendation with Graph Contrastive Learning—0
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
Personalized Prompt for Sequential Recommendation—0
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential RecommendationCode1
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
PAS: A Position-Aware Similarity Measurement for Sequential Recommendation—0
Selective Fairness in Recommendation via PromptsCode1
When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential RecommendationCode1
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
Modeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationCode0
Learning to Augment for Casual User Recommendation—0
Sequential Recommendation with Causal Behavior Discovery—0
Sequential Recommendation with User Evolving Preference Decomposition—0
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential RecommendationCode1
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