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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

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Showing 201–210 of 554 papers

TitleStatusHype
Improving Transformer-based Sequential Recommenders through Preference Editing—0
Image Fusion for Cross-Domain Sequential Recommendation—0
Improve Temporal Awareness of LLMs for Sequential Recommendation—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
Semantic Codebook Learning for Dynamic Recommendation Models—0
Improving LLM Interpretability and Performance via Guided Embedding Refinement for Sequential Recommendation—0
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation—0
CoST: Contrastive Quantization based Semantic Tokenization for Generative Recommendation—0
Attention-based sequential recommendation system using multimodal data—0
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement—0
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