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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 181190 of 554 papers

TitleStatusHype
SLMRec: Distilling Large Language Models into Small for Sequential RecommendationCode1
Dataset Regeneration for Sequential RecommendationCode2
Multi-Behavior Generative RecommendationCode2
Look into the Future: Deep Contextualized Sequential Recommendation0
Learning Partially Aligned Item Representation for Cross-Domain Sequential Recommendation0
RecGPT: Generative Pre-training for Text-based RecommendationCode1
Modeling User Fatigue for Sequential RecommendationCode1
Positional encoding is not the same as context: A study on positional encoding for sequential recommendationCode0
Diffusion-based Contrastive Learning for Sequential RecommendationCode1
ID-centric Pre-training for Recommendation0
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