169 citations · 509 across the 21 of their papers we have counts for
23 papers
HiNet: Novel Multi-Scenario & Multi-Task Learning with Hierarchical Information Extraction
Jie Zhou, Xianshuai Cao, Wenhao Li +4
Multi-scenario & multi-task learning has been widely applied to many recommendation systems in industrial applications, wherein an effective and practical approach is to carry out…
A Review-aware Graph Contrastive Learning Framework for Recommendation
Jie Shuai, Kun Zhang, Le Wu +4
Most modern recommender systems predict users preferences with two components: user and item embedding learning, followed by the user-item interaction modeling. By utilizing the au…
DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
Shulan Ruan, Yong Zhang, Kun Zhang +4
Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually…
LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching
Kun Zhang, Guangyi Lv, Le Wu +3
Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Langu…
AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
Biwei Huang, Fan Feng, Chaochao Lu +2
One practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this paper, we propose a principled framework for adapt…
Graph Domain Adaptation: A Generative View
Ruichu Cai, Fengzhu Wu, Zijian Li +3
Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is im…