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Neural Hierarchical Factorization Machines for User's Event Sequence Analysis
Dongbo Xi, Fuzhen Zhuang, Bowen Song +6
Many prediction tasks of real-world applications need to model multi-order feature interactions in user's event sequence for better detection performance. However, existing popular…
Personalized Transfer of User Preferences for Cross-domain Recommendation
Yongchun Zhu, Zhenwei Tang, Yudan Liu +5
Cold-start problem is still a very challenging problem in recommender systems. Fortunately, the interactions of the cold-start users in the auxiliary source domain can help cold-st…
Deep Subdomain Adaptation Network for Image Classification
Yongchun Zhu, Fuzhen Zhuang, Jindong Wang +5
For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. Previous deep domain adaptation methods mainly learn a…
Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising
Yongchun Zhu, Yudan Liu, Ruobing Xie +6
In recommender systems and advertising platforms, marketers always want to deliver products, contents, or advertisements to potential audiences over media channels such as display,…
Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising
Dongbo Xi, Zhen Chen, Peng Yan +4
In most real-world large-scale online applications (e.g., e-commerce or finance), customer acquisition is usually a multi-step conversion process of audiences. For example, an impr…
Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks
Yongchun Zhu, Ruobing Xie, Fuzhen Zhuang +5
Recently, embedding techniques have achieved impressive success in recommender systems. However, the embedding techniques are data demanding and suffer from the cold-start problem.…
Transfer-Meta Framework for Cross-domain Recommendation to Cold-Start Users
Yongchun Zhu, Kaikai Ge, Fuzhen Zhuang +5
Cold-start problems are enormous challenges in practical recommender systems. One promising solution for this problem is cross-domain recommendation (CDR) which leverages rich info…
Combat Data Shift in Few-shot Learning with Knowledge Graph
Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang +4
Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learn…