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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 221230 of 554 papers

TitleStatusHype
Attacking Pre-trained RecommendationCode0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential RecommendationCode0
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential RecommendationCode0
Equivariant Contrastive Learning for Sequential RecommendationCode0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial ActionsCode0
PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender SystemCode0
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item PredictionCode0
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningCode0
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