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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 7180 of 554 papers

TitleStatusHype
SSDRec: Self-Augmented Sequence Denoising for Sequential RecommendationCode1
Sequence-level Semantic Representation Fusion for Recommender SystemsCode1
Personalized Behavior-Aware Transformer for Multi-Behavior Sequential RecommendationCode1
A Survey on Cross-Domain Sequential RecommendationCode1
Plug-in Diffusion Model for Sequential RecommendationCode1
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k RecommendationCode1
An Attentive Inductive Bias for Sequential Recommendation beyond the Self-AttentionCode1
Context-Aware Sequential Model for Multi-Behaviour RecommendationCode1
Debiasing Sequential Recommenders through Distributionally Robust Optimization over System ExposureCode1
RecJPQ: Training Large-Catalogue Sequential RecommendersCode1
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