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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 471480 of 554 papers

TitleStatusHype
Calibration-Disentangled Learning and Relevance-Prioritized Reranking for Calibrated Sequential RecommendationCode0
Semantic Gaussian Mixture Variational Autoencoder for Sequential RecommendationCode0
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential RecommendationCode0
A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot TransferCode0
CSSR: A Context-Aware Sequential Software Service Recommendation ModelCode0
Memory Augmented Graph Neural Networks for Sequential RecommendationCode0
Unsupervised Graph Embeddings for Session-based Recommendation with Item FeaturesCode0
Are LLM-based Recommenders Already the Best? Simple Scaled Cross-entropy Unleashes the Potential of Traditional Sequential RecommendersCode0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
ARERec: Attentive Local Interaction Model for Sequential RecommendationCode0
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