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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 391400 of 554 papers

TitleStatusHype
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation0
Factorial User Modeling with Hierarchical Graph Neural Network for Enhanced Sequential RecommendationCode0
A Systematic Review and Replicability Study of BERT4Rec for Sequential RecommendationCode1
Multi-Behavior Hypergraph-Enhanced Transformer for Sequential RecommendationCode1
Sequential Recommendation Model for Next Purchase PredictionCode1
Effective and Efficient Training for Sequential Recommendation using Recency SamplingCode1
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential RecommendationCode0
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential RecommendationCode1
Recommender Transformers with Behavior Pathways0
ID-Agnostic User Behavior Pre-training for Sequential Recommendation0
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