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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 101110 of 554 papers

TitleStatusHype
gSASRec: Reducing Overconfidence in Sequential Recommendation Trained with Negative SamplingCode1
Knowledge Prompt-tuning for Sequential RecommendationCode1
Online Distillation-enhanced Multi-modal Transformer for Sequential RecommendationCode1
Reciprocal Sequential RecommendationCode1
Efficient Failure Pattern Identification of Predictive AlgorithmsCode1
TransAct: Transformer-based Realtime User Action Model for Recommendation at PinterestCode1
Text Is All You Need: Learning Language Representations for Sequential RecommendationCode1
When Search Meets Recommendation: Learning Disentangled Search Representation for RecommendationCode1
Graph Masked Autoencoder for Sequential RecommendationCode1
Self-Supervised Multi-Modal Sequential RecommendationCode1
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