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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 521530 of 554 papers

TitleStatusHype
Your Causal Self-Attentive Recommender Hosts a Lonely NeighborhoodCode0
GenRec: Generative Sequential Recommendation with Large Language ModelsCode0
Unconditional Diffusion for Generative Sequential RecommendationCode0
AutoSeqRec: Autoencoder for Efficient Sequential RecommendationCode0
The Whole is Better than the Sum: Using Aggregated Demonstrations in In-Context Learning for Sequential RecommendationCode0
KATRec: Knowledge Aware aTtentive Sequential RecommendationsCode0
Towards Open-world Cross-Domain Sequential Recommendation: A Model-Agnostic Contrastive Denoising ApproachCode0
Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational TransformerCode0
Improving Sequential Recommendations via Bidirectional Temporal Data Augmentation with Pre-trainingCode0
MV-RNN: A Multi-View Recurrent Neural Network for Sequential RecommendationCode0
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