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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 211–220 of 554 papers

TitleStatusHype
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation—0
Freudian and Newtonian Recurrent Cell for Sequential Recommendation—0
A Framework for Elastic Adaptation of User Multiple Intents in Sequential Recommendation—0
Fisher-Weighted Merge of Contrastive Learning Models in Sequential Recommendation—0
A Systematic Replicability and Comparative Study of BSARec and SASRec for Sequential Recommendation—0
Filtering with Time-frequency Analysis: An Adaptive and Lightweight Model for Sequential Recommender Systems Based on Discrete Wavelet Transform—0
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
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