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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 381–390 of 554 papers

TitleStatusHype
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential RecommendationCode0
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
Towards Efficient and Effective Adaptation of Large Language Models for Sequential Recommendation—0
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation—0
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation—0
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language ModelingCode0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation—0
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential RecommendationCode0
Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach—0
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task LearningCode0
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