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 1120 of 554 papers

TitleStatusHype
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation AlgorithmsCode2
RecGPT: A Foundation Model for Sequential RecommendationCode2
Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement LearningCode2
Dataset Regeneration for Sequential RecommendationCode2
End-to-end Learnable Clustering for Intent Learning in RecommendationCode2
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential RecommendationCode2
OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender SystemsCode2
Improving Sequential Recommendations with LLMsCode2
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential RecommendationCode2
IDGenRec: LLM-RecSys Alignment with Textual ID LearningCode2
Show:102550
← PrevPage 2 of 56Next →

No leaderboard results yet.