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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 181–190 of 554 papers

TitleStatusHype
Contrastive Learning for Sequential RecommendationCode1
MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential RecommendationCode1
S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information MaximizationCode1
Sequential Recommendation with Self-Attentive Multi-Adversarial NetworkCode1
Controllable Multi-Interest Framework for RecommendationCode1
A Generic Network Compression Framework for Sequential Recommender SystemsCode1
Advances in Collaborative Filtering and RankingCode1
TiSASRec: Time Interval Aware Self-Attention for Sequential RecommendationCode1
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and EvaluationsCode1
Self-Attentive Sequential RecommendationCode1
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