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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

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Showing 331–340 of 554 papers

TitleStatusHype
Pay Attention to Attention for Sequential Recommendation—0
Personalized next-best action recommendation with multi-party interaction learning for automated decision-making—0
Personalized Prompt for Sequential Recommendation—0
PKGM: A Pre-trained Knowledge Graph Model for E-commerce Application—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
PRECISE: Pre-training Sequential Recommenders with Collaborative and Semantic Information—0
Precision Profile Pollution Attack on Sequential Recommenders via Influence Function—0
An Efficient Continuous Control Perspective for Reinforcement-Learning-based Sequential Recommendation—0
A novel diffusion recommendation algorithm based on multi-scale cnn and residual lstm—0
A Novel Mamba-based Sequential Recommendation Method—0
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