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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 5160 of 554 papers

TitleStatusHype
LLM-based User Profile Management for Recommender System0
Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?Code1
Unsupervised Graph Embeddings for Session-based Recommendation with Item FeaturesCode0
Breaking the Clusters: Uniformity-Optimization for Text-Based Sequential RecommendationCode0
A Contextual-Aware Position Encoding for Sequential RecommendationCode1
SS4Rec: Continuous-Time Sequential Recommendation with State Space ModelsCode0
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential RecommendationCode0
A Zero-Shot Generalization Framework for LLM-Driven Cross-Domain Sequential Recommendation0
Improving Minimax Group Fairness in Sequential RecommendationCode0
Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation0
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