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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

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Showing 531–540 of 554 papers

TitleStatusHype
A Review-Driven Neural Model for Sequential Recommendation—0
Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs—0
Hierarchical Gating Networks for Sequential RecommendationCode0
Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation—0
DeepRec: An Open-source Toolkit for Deep Learning based RecommendationCode0
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs—0
Topic-Enhanced Memory Networks for Personalised Point-of-Interest RecommendationCode0
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and EvaluationsCode1
Hierarchical Context enabled Recurrent Neural Network for RecommendationCode0
BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from TransformerCode0
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