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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 251260 of 554 papers

TitleStatusHype
AutoSeqRec: Autoencoder for Efficient Sequential RecommendationCode0
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential RecommendationCode0
Are LLM-based Recommenders Already the Best? Simple Scaled Cross-entropy Unleashes the Potential of Traditional Sequential RecommendersCode0
Modeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationCode0
Dual-interest Factorization-heads Attention for Sequential RecommendationCode0
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential RecommendationCode0
Mutual Harmony: Sequential Recommendation with Dual Contrastive NetworkCode0
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
A Pre-trained Sequential Recommendation Framework: Popularity Dynamics for Zero-shot TransferCode0
Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item PredictionCode0
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