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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 301–310 of 554 papers

TitleStatusHype
Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive LearningCode0
Modeling Domain and Feedback Transitions for Cross-Domain Sequential Recommendation—0
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation—0
Modeling Sequences as Star Graphs to Address Over-smoothing in Self-attentive Sequential Recommendation—0
Modeling Temporal Positive and Negative Excitation for Sequential Recommendation—0
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation—0
MOI-Mixer: Improving MLP-Mixer with Multi Order Interactions in Sequential Recommendation—0
Molar: Multimodal LLMs with Collaborative Filtering Alignment for Enhanced Sequential Recommendation—0
AdaptiveRec: Adaptively Construct Pairs for Contrastive Learning in Sequential Recommendation—0
Multi-intent Aware Contrastive Learning for Sequential Recommendation—0
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