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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 8190 of 554 papers

TitleStatusHype
LLaRA: Large Language-Recommendation AssistantCode1
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential RecommendationCode1
Collaborative Word-based Pre-trained Item Representation for Transferable RecommendationCode1
Mixed Attention Network for Cross-domain Sequential RecommendationCode1
Rethinking Cross-Domain Sequential Recommendation under Open-World AssumptionsCode1
APGL4SR: A Generic Framework with Adaptive and Personalized Global Collaborative Information in Sequential RecommendationCode1
Large Language Model Can Interpret Latent Space of Sequential RecommenderCode1
Generate What You Prefer: Reshaping Sequential Recommendation via Guided DiffusionCode1
Intent Contrastive Learning with Cross Subsequences for Sequential RecommendationCode1
To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential RecommendersCode1
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