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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 471480 of 554 papers

TitleStatusHype
Position-enhanced and Time-aware Graph Convolutional Network for Sequential RecommendationsCode1
Sequential Recommendation with Graph Neural NetworksCode1
Improving Sequential Recommendation Consistency with Self-Supervised Imitation0
Improving Transformer-based Sequential Recommenders through Preference Editing0
A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-SharingCode1
Modeling Sequences as Distributions with Uncertainty for Sequential RecommendationCode1
Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential RecommendationCode1
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate PredictionCode1
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention0
Linear-Time Self Attention with Codeword Histogram for Efficient RecommendationCode1
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