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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

Showing 171180 of 554 papers

TitleStatusHype
RecGPT: Generative Pre-training for Text-based RecommendationCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Intent-aware Diffusion with Contrastive Learning for Sequential RecommendationCode1
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential RecommendationCode1
Effective and Efficient Training for Sequential Recommendation using Recency SamplingCode1
CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-AttentionCode1
Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-CommerceCode1
Efficient Failure Pattern Identification of Predictive AlgorithmsCode1
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed VideosCode1
Towards Out-of-Distribution Sequential Event Prediction: A Causal TreatmentCode1
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