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.
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