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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 3140 of 554 papers

TitleStatusHype
A Contextual-Aware Position Encoding for Sequential RecommendationCode1
Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential RecommendationCode1
RaSeRec: Retrieval-Augmented Sequential RecommendationCode1
Augmenting Sequential Recommendation with Balanced Relevance and DiversityCode1
Scaling Sequential Recommendation Models with TransformersCode1
Temporal Linear Item-Item Model for Sequential RecommendationCode1
Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language ModelsCode1
Oracle-guided Dynamic User Preference Modeling for Sequential RecommendationCode1
LinRec: Linear Attention Mechanism for Long-term Sequential Recommender SystemsCode1
SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item SimilarityCode1
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