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 491–500 of 554 papers

TitleStatusHype
Improve Temporal Awareness of LLMs for Sequential Recommendation—0
Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation—0
Improving Micro-video Recommendation by Controlling Position Bias—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation—0
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks—0
Improving Transformer-based Sequential Recommenders through Preference Editing—0
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation—0
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation—0
Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels—0
Show:102550
← PrevPage 50 of 56Next →

No leaderboard results yet.