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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 271280 of 554 papers

TitleStatusHype
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential RecommendationCode0
AutoSeqRec: Autoencoder for Efficient Sequential RecommendationCode0
Hierarchical Gating Networks for Sequential RecommendationCode0
Mutual Harmony: Sequential Recommendation with Dual Contrastive NetworkCode0
Sequential Recommendation with Controllable Diversification: Representation Degeneration and DiversityCode0
Dual-interest Factorization-heads Attention for Sequential RecommendationCode0
Behavior-Dependent Linear Recurrent Units for Efficient Sequential RecommendationCode0
Multi-Level Sequence Denoising with Cross-Signal Contrastive Learning for Sequential RecommendationCode0
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential RecommendationCode0
GenRec: Generative Sequential Recommendation with Large Language ModelsCode0
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