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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 511520 of 554 papers

TitleStatusHype
KATRec: Knowledge Aware aTtentive Sequential RecommendationsCode0
XDM: Improving Sequential Deep Matching with Unclicked User Behaviors for Recommender SystemCode0
TRec: Sequential Recommender Based On Latent Item Trend Information0
Sequential recommendation with metric models based on frequent sequences0
Learning Post-Hoc Causal Explanations for Recommendation0
Self-Supervised Reinforcement Learning for Recommender Systems0
Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective0
Inter-sequence Enhanced Framework for Personalized Sequential Recommendation0
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval0
HAM: Hybrid Associations Models for Sequential RecommendationCode0
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