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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 201210 of 554 papers

TitleStatusHype
CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential RecommendationCode0
Cracking the Code of Negative Transfer: A Cooperative Game Theoretic Approach for Cross-Domain Sequential RecommendationCode0
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task LearningCode0
Aligning GPTRec with Beyond-Accuracy Goals with Reinforcement LearningCode0
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
CosRec: 2D Convolutional Neural Networks for Sequential RecommendationCode0
Attacking Pre-trained RecommendationCode0
One Person, One Model--Learning Compound Router for Sequential RecommendationCode0
Neighborhood-based Hard Negative Mining for Sequential RecommendationCode0
Contrastive Enhanced Slide Filter Mixer for Sequential RecommendationCode0
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