H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 37 Citations 5,627 176 World Ranking 5352 National Ranking 150

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Hongzhi Yin focuses on Artificial intelligence, Machine learning, Recommender system, Data mining and Information retrieval. His study in the field of Deep learning and Feature extraction also crosses realms of Check-in and Space. His Machine learning research includes themes of Social media, Probabilistic logic, Inference and Social network.

His biological study spans a wide range of topics, including Mobile device, Graph embedding, Graph and Graph based. His work on Collaborative filtering as part of general Recommender system study is frequently linked to Location-based service, bridging the gap between disciplines. His research integrates issues of Mixture model, Spectral clustering, Leverage and Graph partition in his study of Data mining.

His most cited work include:

  • LCARS: a location-content-aware recommender system (252 citations)
  • Learning Graph-based POI Embedding for Location-based Recommendation (190 citations)
  • Challenging the long tail recommendation (148 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Machine learning, Recommender system, Information retrieval and Data mining. In general Artificial intelligence, his work in Deep learning, Feature learning, Embedding and Inference is often linked to Focus linking many areas of study. Hongzhi Yin has researched Machine learning in several fields, including Point of interest and Group.

His work on Collaborative filtering and Cold start as part of general Recommender system study is frequently connected to Location-based service, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. Hongzhi Yin has included themes like Structure and User profile in his Information retrieval study. His Social network research is multidisciplinary, incorporating elements of Data science and Graph.

He most often published in these fields:

  • Artificial intelligence (38.32%)
  • Machine learning (31.78%)
  • Recommender system (30.84%)

What were the highlights of his more recent work (between 2020-2021)?

  • Artificial intelligence (38.32%)
  • Recommender system (30.84%)
  • Machine learning (31.78%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Artificial intelligence, Recommender system, Machine learning, Feature learning and Embedding. The Deep learning and Inference research he does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as User modeling and Work, therefore creating a link between diverse domains of science. Hongzhi Yin focuses mostly in the field of Recommender system, narrowing it down to matters related to Computer security and, in some cases, Information overload.

His studies deal with areas such as Classifier and Representation as well as Machine learning. His work carried out in the field of Feature learning brings together such families of science as Anomaly detection, Theoretical computer science and Snapshot. His research in Embedding intersects with topics in Relevance and Information needs.

Between 2020 and 2021, his most popular works were:

  • Heterogeneous Hypergraph Embedding for Graph Classification (4 citations)
  • Hierarchical Hyperedge Embedding-based Representation Learning for Group Recommendation. (1 citations)
  • Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (1 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Algorithm

His primary areas of study are Embedding, Theoretical computer science, Recommender system, Machine learning and Artificial intelligence. His study in Embedding is interdisciplinary in nature, drawing from both Feature learning and Metric. The study incorporates disciplines such as Space, Field and Domain in addition to Theoretical computer science.

His Recommender system study combines topics from a wide range of disciplines, such as Question generation, Human–computer interaction and Knowledge graph. Hongzhi Yin is interested in Cold start, which is a field of Machine learning. His Artificial intelligence research is multidisciplinary, relying on both Relation and Group.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

LCARS: a location-content-aware recommender system

Hongzhi Yin;Yizhou Sun;Bin Cui;Zhiting Hu.
knowledge discovery and data mining (2013)

372 Citations

Learning Graph-based POI Embedding for Location-based Recommendation

Min Xie;Hongzhi Yin;Hao Wang;Fanjiang Xu.
conference on information and knowledge management (2016)

296 Citations

Adapting to User Interest Drift for POI Recommendation

Hongzhi Yin;Xiaofang Zhou;Bin Cui;Hao Wang.
IEEE Transactions on Knowledge and Data Engineering (2016)

207 Citations

Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation

Hongzhi Yin;Weiqing Wang;Hao Wang;Ling Chen.
IEEE Transactions on Knowledge and Data Engineering (2017)

173 Citations

A temporal context-aware model for user behavior modeling in social media systems

Hongzhi Yin;Bin Cui;Ling Chen;Zhiting Hu.
international conference on management of data (2014)

156 Citations

LCARS: A Spatial Item Recommender System

Hongzhi Yin;Bin Cui;Yizhou Sun;Zhiting Hu.
ACM Transactions on Information Systems (2014)

153 Citations

Challenging the long tail recommendation

Hongzhi Yin;Bin Cui;Jing Li;Junjie Yao.
very large data bases (2012)

148 Citations

Dynamic User Modeling in Social Media Systems

Hongzhi Yin;Bin Cui;Ling Chen;Zhiting Hu.
ACM Transactions on Information Systems (2015)

148 Citations

Modeling Location-Based User Rating Profiles for Personalized Recommendation

Hongzhi Yin;Bin Cui;Ling Chen;Zhiting Hu.
ACM Transactions on Knowledge Discovery From Data (2015)

139 Citations

Discovering interpretable geo-social communities for user behavior prediction

Hongzhi Yin;Zhiting Hu;Xiaofang Zhou;Hao Wang.
international conference on data engineering (2016)

132 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

If you think any of the details on this page are incorrect, let us know.

Contact us

Top Scientists Citing Hongzhi Yin

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 28

Xiangnan He

Xiangnan He

University of Science and Technology of China

Publications: 27

Yong Li

Yong Li

Tsinghua University

Publications: 23

Hui Xiong

Hui Xiong

Rutgers, The State University of New Jersey

Publications: 19

Xiaofang Zhou

Xiaofang Zhou

Hong Kong University of Science and Technology

Publications: 15

Gao Cong

Gao Cong

Nanyang Technological University

Publications: 15

Guandong Xu

Guandong Xu

University of Technology Sydney

Publications: 15

Tat-Seng Chua

Tat-Seng Chua

National University of Singapore

Publications: 15

Lina Yao

Lina Yao

UNSW Sydney

Publications: 14

Irwin King

Irwin King

Chinese University of Hong Kong

Publications: 14

Chuan Shi

Chuan Shi

Beijing University of Posts and Telecommunications

Publications: 13

Enhong Chen

Enhong Chen

University of Science and Technology of China

Publications: 13

Michael R. Lyu

Michael R. Lyu

Chinese University of Hong Kong

Publications: 12

Yan Wang

Yan Wang

Macquarie University

Publications: 12

Zi Huang

Zi Huang

University of Queensland

Publications: 12

Something went wrong. Please try again later.