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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 526–550 of 554 papers

TitleStatusHype
Cascading: Association Augmented Sequential Recommendation—0
Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations—0
SSE-PT: Sequential Recommendation Via Personalized TransformerCode0
CosRec: 2D Convolutional Neural Networks for Sequential RecommendationCode0
Improving End-to-End Sequential Recommendations with Intent-aware DiversificationCode0
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
Towards Neural Mixture Recommender for Long Range Dependent User Sequences—0
Adaptive User Modeling with Long and Short-Term Preferences for Personalized RecommendationCode0
MV-RNN: A Multi-View Recurrent Neural Network for Sequential RecommendationCode0
An Analysis of Approaches Taken in the ACM RecSys Challenge 2018 for Automatic Music Playlist Continuation—0
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks—0
Personalized Top-N Sequential Recommendation via Convolutional Sequence EmbeddingCode0
Self-Attentive Sequential RecommendationCode1
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation—0
Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation—0
Superposition-Assisted Stochastic Optimization for Hawkes Processes—0
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