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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 451–460 of 554 papers

TitleStatusHype
Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation—0
FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings—0
Farzi Data: Autoregressive Data Distillation—0
SC-Rec: Enhancing Generative Retrieval with Self-Consistent Reranking for Sequential Recommendation—0
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
Few-shot Model Extraction Attacks against Sequential Recommender Systems—0
Towards Differential Privacy in Sequential Recommendation: A Noisy Graph Neural Network Approach—0
Filtering with Time-frequency Analysis: An Adaptive and Lightweight Model for Sequential Recommender Systems Based on Discrete Wavelet Transform—0
Self-supervised Learning for Sequential Recommendation with Model Augmentation—0
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