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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 301310 of 554 papers

TitleStatusHype
Knowledge Prompt-tuning for Sequential RecommendationCode1
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative SamplingCode1
AutoSeqRec: Autoencoder for Efficient Sequential RecommendationCode0
SSLRec: A Self-Supervised Learning Framework for RecommendationCode2
Online Distillation-enhanced Multi-modal Transformer for Sequential RecommendationCode1
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential RecommendationCode0
Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation0
Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation0
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning0
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