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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 541–550 of 554 papers

TitleStatusHype
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
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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