6 citations · 19 across the 8 of their papers we have counts for
8 papers
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Pan Li, Maofei Que, Zhichao Jiang +2
Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. T…
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
Pan Li, Zhichao Jiang, Maofei Que +2
Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem by aggregating and transferring user preferences across multiple categor…
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework
Han Zhang, Wenhao Zheng, Charley Chen +4
Since the label collecting is prohibitive and time-consuming, unsupervised methods are preferred in applications such as fraud detection. Meanwhile, such applications usually requi…
LAMP: Label Augmented Multimodal Pretraining
Jia Guo, Chen Zhu, Yilun Zhao +4
Multi-modal representation learning by pretraining has become an increasing interest due to its easy-to-use and potential benefit for various Visual-and-Language~(V-L) tasks. Howev…
Multi-label Zero-shot Classification by Learning to Transfer from External Knowledge
He Huang, Yuanwei Chen, Wei Tang +4
Multi-label zero-shot classification aims to predict multiple unseen class labels for an input image. It is more challenging than its single-label counterpart. On one hand, the unc…
Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution
Shu-Ting Shi, Wenhao Zheng, Jun Tang +4
Click-through rate (CTR) prediction is an essential task in industrial applications such as video recommendation. Recently, deep learning models have been proposed to learn the rep…
Knowledge Amalgamation from Heterogeneous Networks by Common Feature Learning
Sihui Luo, Xinchao Wang, Gongfan Fang +3
An increasing number of well-trained deep networks have been released online by researchers and developers, enabling the community to reuse them in a plug-and-play way without acce…
Multi-Objective Generalized Linear Bandits
Shiyin Lu, Guanghui Wang, Yao Hu +1
In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objec…