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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 110 of 554 papers

TitleStatusHype
Revisiting Self-Attentive Sequential RecommendationCode4
HLLM: Enhancing Sequential Recommendations via Hierarchical Large Language Models for Item and User ModelingCode4
Data Augmentation for Sequential Recommendation: A SurveyCode3
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential RecommendationCode2
RecGPT: A Foundation Model for Sequential RecommendationCode2
Rec-R1: Bridging Generative Large Language Models and User-Centric Recommendation Systems via Reinforcement LearningCode2
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential RecommendationCode2
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential RecommendationCode2
SelfGNN: Self-Supervised Graph Neural Networks for Sequential RecommendationCode2
Dataset Regeneration for Sequential RecommendationCode2
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