Taobao
Canonical16papers using it
2016first seen
The 'Taobao' dataset is used to evaluate lifelong user interest modeling in recommender systems, containing user behavior sequences for display advertising.
Papers using Taobao (16)
- Modeling Behavioral Intensity and Transitions for Generative RecommendationSWGCN: Synergy Weighted Graph Convolutional Network for Multi-Behavior RecommendationPI2I: A Personalized Item-Based Collaborative Filtering Retrieval FrameworkReaSeq: Unleashing World Knowledge via Reasoning for Sequential ModelingSaviorRec: Semantic-Behavior Alignment for Cold-Start RecommendationTBGRecall: A Generative Retrieval Model for E-commerce Recommendation ScenariosUser Long-Term Multi-Interest Retrieval Model for RecommendationMoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert SpecializationControllable Multi-Interest Framework for RecommendationContext-aware Sequential RecommendationExploring Periodicity and Interactivity in Multi-Interest Framework for
Sequential RecommendationEdgeRec: Recommender System on Edge in Mobile TaobaoA Brand-level Ranking System with the Customized Attention-GRU ModelRNE: A Scalable Network Embedding for Billion-scale RecommendationCascading: Association Augmented Sequential RecommendationCTR is not Enough: a Novel Reinforcement Learning based Ranking Approach
for Optimizing Session Clicks