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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 151160 of 554 papers

TitleStatusHype
Dynamic Memory based Attention Network for Sequential RecommendationCode1
EAGER: Two-Stream Generative Recommender with Behavior-Semantic CollaborationCode1
Filter-enhanced MLP is All You Need for Sequential RecommendationCode1
Large Language Model Can Interpret Latent Space of Sequential RecommenderCode1
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential RecommendationCode1
E4SRec: An Elegant Effective Efficient Extensible Solution of Large Language Models for Sequential RecommendationCode1
Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential RecommendationCode1
Leveraging Large Language Models for Sequential RecommendationCode1
Frequency Enhanced Hybrid Attention Network for Sequential RecommendationCode1
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTCode1
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