Search Results for author: Vincent W. Zheng

Found 14 papers, 6 papers with code

Practical and Secure Federated Recommendation with Personalized Masks

no code implementations18 Aug 2021 Liu Yang, Ben Tan, Bo Liu, Vincent W. Zheng, Kai Chen, Qiang Yang

Federated masked matrix factorization could protect the data privacy in federated recommender systems without sacrificing efficiency or efficacy.

Federated Learning Recommendation Systems

Adam revisited: a weighted past gradients perspective

no code implementations1 Jan 2021 Hui Zhong, Zaiyi Chen, Chuan Qin, Zai Huang, Vincent W. Zheng, Tong Xu, Enhong Chen

Though many algorithms, such as AMSGRAD and ADAMNC, have been proposed to fix the non-convergence issues, achieving a data-dependent regret bound similar to or better than ADAGRAD is still a challenge to these methods.

Privacy Threats Against Federated Matrix Factorization

no code implementations3 Jul 2020 Dashan Gao, Ben Tan, Ce Ju, Vincent W. Zheng, Qiang Yang

Matrix Factorization has been very successful in practical recommendation applications and e-commerce.

Collaborative Filtering Federated Learning +1

Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach

no code implementations30 May 2019 Min Hou, Le Wu, Enhong Chen, Zhi Li, Vincent W. Zheng, Qi Liu

When making cloth decisions, people usually show preferences for different semantic attributes (e. g., the clothes with v-neck collar).

Ranked #2 on Recommendation Systems on Amazon Fashion (using extra training data)

Recommendation Systems

Beyond Personalization: Social Content Recommendation for Creator Equality and Consumer Satisfaction

1 code implementation28 May 2019 Wenyi Xiao, Huan Zhao, Haojie Pan, Yangqiu Song, Vincent W. Zheng, Qiang Yang

An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content.

Deep Transfer Learning for Cross-domain Activity Recognition

no code implementations20 Jul 2018 Jindong Wang, Vincent W. Zheng, Yiqiang Chen, Meiyu Huang

In this paper, we propose an effective Unsupervised Source Selection algorithm for Activity Recognition (USSAR).

Cross-Domain Activity Recognition Transfer Learning

CityTransfer: Transferring Inter- and Intra-City Knowledge for Chain Store Site Recommendation based on Multi-Source Urban Data

1 code implementation 2018 Bin Guo, Jing Li, Vincent W. Zheng, Zhu Wang, Zhiwen Yu

To solve the cold-start problem, we propose CityTransfer, which transfers chain store knowledge from semantically-relevant domains (e. g., other cities with rich knowledge, similar chain enterprises in the target city) for chain store placement recommendation in a new city.

Collaborative Filtering Transfer Learning

Topological Recurrent Neural Network for Diffusion Prediction

1 code implementation28 Nov 2017 Jia Wang, Vincent W. Zheng, Zemin Liu, Kevin Chen-Chuan Chang

As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure.

Representation Learning

Active Learning for Graph Embedding

1 code implementation15 May 2017 Hongyun Cai, Vincent W. Zheng, Kevin Chen-Chuan Chang

Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information.

Active Learning Graph Embedding +1

From Community Detection to Community Profiling

no code implementations17 Jan 2017 Hongyun Cai, Vincent W. Zheng, Fanwei Zhu, Kevin Chen-Chuan Chang, Zi Huang

Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links.

Community Detection

From Node Embedding To Community Embedding

2 code implementations31 Oct 2016 Vincent W. Zheng, Sandro Cavallari, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria

Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being "close" on the graph are close too in the low-dimensional space.

Graph Embedding Node Classification

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