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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 511520 of 554 papers

TitleStatusHype
Adaptive Long-term Embedding with Denoising and Augmentation for RecommendationCode0
Improving Sequential Recommenders through Counterfactual Augmentation of System ExposureCode0
Topic-Enhanced Memory Networks for Personalised Point-of-Interest RecommendationCode0
UFNRec: Utilizing False Negative Samples for Sequential RecommendationCode0
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential RecommendationCode0
Privacy-Preserving Cross-Domain Sequential RecommendationCode0
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential RecommendationCode0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
Prompt-based Multi-interest Learning Method for Sequential RecommendationCode0
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential RecommendationCode0
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