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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 461–470 of 554 papers

TitleStatusHype
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings—0
Designing a Sequential Recommendation System for Heterogeneous Interactions Using Transformers—0
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation—0
Learnable Model Augmentation Self-Supervised Learning for Sequential Recommendation—0
Coarse-to-Fine Sparse Sequential Recommendation—0
Learning to Augment for Casual User Recommendation—0
Modeling Dynamic User Preference via Dictionary Learning for Sequential RecommendationCode0
Sequential Recommendation with Causal Behavior Discovery—0
Sequential Recommendation with User Evolving Preference Decomposition—0
ARERec: Attentive Local Interaction Model for Sequential RecommendationCode0
Show:102550
← PrevPage 47 of 56Next →

No leaderboard results yet.