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
SIGMA: Selective Gated Mamba for Sequential RecommendationCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
Harnessing Multimodal Large Language Models for Multimodal Sequential RecommendationCode1
Pacer and Runner: Cooperative Learning Framework between Single- and Cross-Domain Sequential RecommendationCode1
UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential RecommendationsCode1
EAGER: Two-Stream Generative Recommender with Behavior-Semantic CollaborationCode1
SLMRec: Distilling Large Language Models into Small for Sequential RecommendationCode1
RecGPT: Generative Pre-training for Text-based RecommendationCode1
Modeling User Fatigue for Sequential RecommendationCode1
Diffusion-based Contrastive Learning for Sequential RecommendationCode1
Show:102550
← PrevPage 6 of 56Next →

No leaderboard results yet.