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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 321330 of 554 papers

TitleStatusHype
TransAct: Transformer-based Realtime User Action Model for Recommendation at PinterestCode1
Robust Reinforcement Learning Objectives for Sequential Recommender SystemsCode0
TriMLP: Revenge of a MLP-like Architecture in Sequential RecommendationCode0
Text Is All You Need: Learning Language Representations for Sequential RecommendationCode1
When Search Meets Recommendation: Learning Disentangled Search Representation for RecommendationCode1
PALR: Personalization Aware LLMs for Recommendation0
Graph Masked Autoencoder for Sequential RecommendationCode1
Contrastive Enhanced Slide Filter Mixer for Sequential RecommendationCode0
Attacking Pre-trained RecommendationCode0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
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