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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 491500 of 554 papers

TitleStatusHype
Sparse-Interest Network for Sequential RecommendationCode1
Sequential Recommendation in Online Games with Multiple Sequences, Tasks and User Levels0
Freudian and Newtonian Recurrent Cell for Sequential Recommendation0
RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential RecommendationCode1
Offline Adaptive Policy Leaning in Real-World Sequential Recommendation Systems0
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative StackingCode1
Cold-start Sequential Recommendation via Meta Learner0
KATRec: Knowledge Aware aTtentive Sequential RecommendationsCode0
Mixed Information Flow for Cross-domain Sequential RecommendationsCode1
RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation AlgorithmsCode2
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