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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 101–110 of 554 papers

TitleStatusHype
Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation—0
SimRec: Mitigating the Cold-Start Problem in Sequential Recommendation by Integrating Item SimilarityCode1
Dual Conditional Diffusion Models for Sequential Recommendation—0
Modeling Temporal Positive and Negative Excitation for Sequential Recommendation—0
Pay Attention to Attention for Sequential Recommendation—0
Generative Diffusion Models for Sequential Recommendations—0
Cross-Domain Sequential Recommendation via Neural Process—0
Generate and Instantiate What You Prefer: Text-Guided Diffusion for Sequential Recommendation—0
Preference Diffusion for RecommendationCode1
Intent-Enhanced Data Augmentation for Sequential Recommendation—0
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