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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
Similarity-Guided Diffusion for Contrastive Sequential Recommendation0
Unconditional Diffusion for Generative Sequential RecommendationCode0
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential RecommendationCode1
DARTS: A Dual-View Attack Framework for Targeted Manipulation in Federated Sequential Recommendation0
A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation0
LLM2Rec: Large Language Models Are Powerful Embedding Models for Sequential RecommendationCode2
C-TLSAN: Content-Enhanced Time-Aware Long- and Short-Term Attention Network for Personalized RecommendationCode0
RecGPT: A Foundation Model for Sequential RecommendationCode2
GLoSS: Generative Language Models with Semantic Search for Sequential RecommendationCode1
Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective ApproachCode1
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