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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 291300 of 554 papers

TitleStatusHype
CPMR: Context-Aware Incremental Sequential Recommendation with Pseudo-Multi-Task LearningCode0
Adaptive Multi-Modalities Fusion in Sequential Recommendation SystemsCode1
A Survey on Multi-Behavior Sequential Recommendation0
RecMind: Large Language Model Powered Agent For Recommendation0
Text Matching Improves Sequential Recommendation by Reducing Popularity BiasesCode1
LLMRec: Benchmarking Large Language Models on Recommendation TaskCode1
Invariant representation learning for sequential recommendation0
MISSRec: Pre-training and Transferring Multi-modal Interest-aware Sequence Representation for RecommendationCode1
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation0
Attention Calibration for Transformer-based Sequential RecommendationCode1
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