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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

Showing 291300 of 554 papers

TitleStatusHype
Improving Sequential Recommenders through Counterfactual Augmentation of System ExposureCode0
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and ItemsCode0
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential RecommendationCode0
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential RecommendationCode0
Empowering Sequential Recommendation from Collaborative Signals and Semantic RelatednessCode0
HTP: Exploiting Holistic Temporal Patterns for Sequential RecommendationCode0
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement LearningCode0
Breaking the Clusters: Uniformity-Optimization for Text-Based Sequential RecommendationCode0
TriMLP: Revenge of a MLP-like Architecture in Sequential RecommendationCode0
Enhancing Sequential Music Recommendation with Personalized Popularity AwarenessCode0
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