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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 441450 of 554 papers

TitleStatusHype
Pattern-wise Transparent Sequential Recommendation0
Pay Attention to Attention for Sequential Recommendation0
Personalized next-best action recommendation with multi-party interaction learning for automated decision-making0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
Equivariant Contrastive Learning for Sequential RecommendationCode0
Breaking the Clusters: Uniformity-Optimization for Text-Based Sequential RecommendationCode0
Symmetry Structured Convolutional Neural NetworksCode0
Sequential Recommendation with Probabilistic Logical ReasoningCode0
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningCode0
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential RecommendationCode0
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