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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

Showing 281290 of 554 papers

TitleStatusHype
Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation0
KuaiSim: A Comprehensive Simulator for Recommender SystemsCode1
Diffusion Augmentation for Sequential RecommendationCode1
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation0
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation0
Reformulating Sequential Recommendation: Learning Dynamic User Interest with Content-enriched Language ModelingCode0
Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential RecommendationCode0
Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach0
Leveraging Large Language Models for Sequential RecommendationCode1
FedDCSR: Federated Cross-domain Sequential Recommendation via Disentangled Representation LearningCode1
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