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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 91100 of 554 papers

TitleStatusHype
A Self-Correcting Sequential RecommenderCode1
Adversarial and Contrastive Variational Autoencoder for Sequential RecommendationCode1
A Survey on Cross-Domain Sequential RecommendationCode1
Generate What You Prefer: Reshaping Sequential Recommendation via Guided DiffusionCode1
Beyond Learning from Next Item: Sequential Recommendation via Personalized Interest SustainabilityCode1
Collaborative Word-based Pre-trained Item Representation for Transferable RecommendationCode1
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich RecommendationCode1
Controllable Multi-Interest Framework for RecommendationCode1
An Empirical Study of Training ID-Agnostic Multi-modal Sequential RecommendersCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
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