SOTAVerified

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 5160 of 554 papers

TitleStatusHype
AdaMCT: Adaptive Mixture of CNN-Transformer for Sequential RecommendationCode1
A Systematic Review and Replicability Study of BERT4Rec for Sequential RecommendationCode1
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment RecommendationCode1
Contrastive Learning for Representation Degeneration Problem in Sequential RecommendationCode1
Attention Calibration for Transformer-based Sequential RecommendationCode1
Attention Mixtures for Time-Aware Sequential RecommendationCode1
Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training TransformerCode1
Augmenting Sequential Recommendation with Balanced Relevance and DiversityCode1
AlphaFuse: Learn ID Embeddings for Sequential Recommendation in Null Space of Language EmbeddingsCode1
CT4Rec: Simple yet Effective Consistency Training for Sequential RecommendationCode1
Show:102550
← PrevPage 6 of 56Next →

No leaderboard results yet.