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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 351–360 of 554 papers

TitleStatusHype
Improving Micro-video Recommendation by Controlling Position Bias—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation—0
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks—0
Improving Transformer-based Sequential Recommenders through Preference Editing—0
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation—0
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation—0
Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels—0
Towards Neural Mixture Recommender for Long Range Dependent User Sequences—0
Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention—0
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