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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 131140 of 554 papers

TitleStatusHype
Diffusion Augmentation for Sequential RecommendationCode1
Data Augmentation as Free Lunch: Exploring the Test-Time Augmentation for Sequential RecommendationCode1
Debiased Contrastive Learning for Sequential RecommendationCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional TransformersCode1
Decoupled Side Information Fusion for Sequential RecommendationCode1
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionCode1
Intent Contrastive Learning with Cross Subsequences for Sequential RecommendationCode1
An Empirical Study of Training ID-Agnostic Multi-modal Sequential RecommendersCode1
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-AttentionCode1
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