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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 31–40 of 554 papers

TitleStatusHype
A Generic Network Compression Framework for Sequential Recommender SystemsCode1
A Contextual-Aware Position Encoding for Sequential RecommendationCode1
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and EvaluationsCode1
Contrastive Learning with Bidirectional Transformers for Sequential RecommendationCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
Adversarial and Contrastive Variational Autoencoder for Sequential RecommendationCode1
Advances in Collaborative Filtering and RankingCode1
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative TransformerCode1
Collaborative Word-based Pre-trained Item Representation for Transferable RecommendationCode1
Attention Calibration for Transformer-based Sequential RecommendationCode1
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