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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 241250 of 554 papers

TitleStatusHype
Improving Sequential Recommendation Models with an Enhanced Loss FunctionCode0
Modeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationCode0
MV-RNN: A Multi-View Recurrent Neural Network for Sequential RecommendationCode0
EchoMamba4Rec: Harmonizing Bidirectional State Space Models with Spectral Filtering for Advanced Sequential RecommendationCode0
ARERec: Attentive Local Interaction Model for Sequential RecommendationCode0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
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
Meta-Learning with Adaptive Weighted Loss for Imbalanced Cold-Start RecommendationCode0
Dual-interest Factorization-heads Attention for Sequential RecommendationCode0
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