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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 331340 of 554 papers

TitleStatusHype
Semantic Retrieval Augmented Contrastive Learning for Sequential Recommendation0
GOT4Rec: Graph of Thoughts for Sequential Recommendation0
SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation0
Seq2seq Translation Model for Sequential Recommendation0
G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer0
GUESR: A Global Unsupervised Data-Enhancement with Bucket-Cluster Sampling for Sequential Recommendation0
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks0
Hierarchical Attention Fusion of Visual and Textual Representations for Cross-Domain Sequential Recommendation0
HeterRec: Heterogeneous Information Transformer for Scalable Sequential Recommendation0
Hierarchical Contrastive Learning with Multiple Augmentation for Sequential Recommendation0
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