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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 351360 of 554 papers

TitleStatusHype
A Self-Correcting Sequential RecommenderCode1
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
Intelligent Model Update Strategy for Sequential Recommendation0
On Modeling Long-Term User Engagement from Stochastic Feedback0
Dual-interest Factorization-heads Attention for Sequential RecommendationCode0
Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution NetworkCode0
Mutual Wasserstein Discrepancy Minimization for Sequential RecommendationCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
Modeling Sequential Recommendation as Missing Information ImputationCode0
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