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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 151–160 of 554 papers

TitleStatusHype
Intent Contrastive Learning for Sequential RecommendationCode1
Sequential Recommendation via Stochastic Self-AttentionCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
RecGURU: Adversarial Learning of Generalized User Representations for Cross-Domain RecommendationCode1
Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-CommerceCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential RecommendationCode1
Black-Box Attacks on Sequential Recommenders via Data-Free Model ExtractionCode1
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential RecommendationCode1
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