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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 371–380 of 554 papers

TitleStatusHype
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential RecommendationCode0
AutoSAM: Towards Automatic Sampling of User Behaviors for Sequential Recommender SystemsCode0
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model—0
Model-enhanced Contrastive Reinforcement Learning for Sequential Recommendation—0
One Model for All: Large Language Models are Domain-Agnostic Recommendation Systems—0
Meta-optimized Joint Generative and Contrastive Learning for Sequential Recommendation—0
Thoroughly Modeling Multi-domain Pre-trained Recommendation as Language—0
Dual-Scale Interest Extraction Framework with Self-Supervision for Sequential Recommendation—0
Farzi Data: Autoregressive Data Distillation—0
Memory efficient location recommendation through proximity-aware representation—0
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