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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 6170 of 554 papers

TitleStatusHype
FineRec:Exploring Fine-grained Sequential RecommendationCode1
The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential RecommendationCode1
IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation with Decoupled PEFTCode1
Sequential Recommendation with Latent Relations based on Large Language ModelCode1
An Empirical Study of Training ID-Agnostic Multi-modal Sequential RecommendersCode1
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential RecommendationCode1
A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment RecommendationCode1
Harnessing Large Language Models for Text-Rich Sequential RecommendationCode1
Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential RecommendationCode1
Repeated Padding for Sequential RecommendationCode1
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