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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 91–100 of 554 papers

TitleStatusHype
Item Association Factorization Mixed Markov Chains for Sequential Recommendation—0
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
Counterfactual Learning-Driven Representation Disentanglement for Search-Enhanced Recommendation—0
LLM-based Bi-level Multi-interest Learning Framework for Sequential Recommendation—0
Efficient and Effective Adaptation of Multimodal Foundation Models in Sequential Recommendation—0
Transferable Sequential Recommendation via Vector Quantized Meta Learning—0
LinRec: Linear Attention Mechanism for Long-term Sequential Recommender SystemsCode1
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation—0
DivNet: Diversity-Aware Self-Correcting Sequential Recommendation Networks—0
Breaking Determinism: Fuzzy Modeling of Sequential Recommendation Using Discrete State Space Diffusion Model—0
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