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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 221–230 of 554 papers

TitleStatusHype
Generating Negative Samples for Sequential Recommendation—0
Generative Diffusion Models for Sequential Recommendations—0
Intent-Interest Disentanglement and Item-Aware Intent Contrastive Learning for Sequential Recommendation—0
Invariant representation learning for sequential recommendation—0
FELLAS: Enhancing Federated Sequential Recommendation with LLM as External Services—0
GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems—0
Federated Mixture-of-Expert for Non-Overlapped Cross-Domain Sequential Recommendation—0
Context-based Fast Recommendation Strategy for Long User Behavior Sequence in Meituan Waimai—0
Farzi Data: Autoregressive Data Distillation—0
Context-aware Sequential Recommendation—0
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