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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 391–400 of 554 papers

TitleStatusHype
A Survey on Multi-Behavior Sequential Recommendation—0
RecMind: Large Language Model Powered Agent For Recommendation—0
Invariant representation learning for sequential recommendation—0
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation—0
AutoSeqRec: Autoencoder for Efficient Sequential RecommendationCode0
Understanding and Modeling Passive-Negative Feedback for Short-video Sequential RecommendationCode0
Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation—0
Disentangled Counterfactual Reasoning for Unbiased Sequential Recommendation—0
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation—0
ClusterSeq: Enhancing Sequential Recommender Systems with Clustering based Meta-Learning—0
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