What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Machine learning
His primary areas of study are Data mining, Information retrieval, World Wide Web, Collaborative filtering and Artificial intelligence.
His Data mining research is multidisciplinary, incorporating elements of Inference and Mobile computing.
When carried out as part of a general Information retrieval research project, his work on Ranking is frequently linked to work in Property, therefore connecting diverse disciplines of study.
The study incorporates disciplines such as Service and Geographic information system in addition to World Wide Web.
His work carried out in the field of Collaborative filtering brings together such families of science as Exploit, Geospatial analysis and Social network.
His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence.
His most cited work include:
- Mining interesting locations and travel sequences from GPS trajectories (1426 citations)
- GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory. (753 citations)
- T-drive: driving directions based on taxi trajectories (721 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Information retrieval, Artificial intelligence, Data mining, World Wide Web and Machine learning.
His work on Information retrieval is being expanded to include thematically relevant topics such as Web page.
His studies deal with areas such as Recommender system, Computer vision and Natural language processing as well as Artificial intelligence.
His studies in Recommender system integrate themes in fields like Variety, Embedding and Knowledge graph.
His research in Data mining intersects with topics in Inference and Social network.
His World Wide Web study frequently draws connections to adjacent fields such as Context.
He most often published in these fields:
- Information retrieval (27.27%)
- Artificial intelligence (25.80%)
- Data mining (16.95%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (25.80%)
- Information retrieval (27.27%)
- Recommender system (14.25%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Information retrieval, Recommender system, Machine learning and Natural language processing.
His Information retrieval research is multidisciplinary, incorporating perspectives in Upload, Word, Leverage and Reading.
His Recommender system research is multidisciplinary, relying on both Variety, Embedding and Knowledge graph.
He combines topics linked to Graph neural networks with his work on Machine learning.
His Natural language processing research integrates issues from Field, Named-entity recognition, Social media and Representation.
The study of Collaborative filtering is intertwined with the study of Social network in a number of ways.
Between 2017 and 2021, his most popular works were:
- xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (230 citations)
- Session-Based Recommendation with Graph Neural Networks (217 citations)
- RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (210 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Artificial intelligence, Information retrieval, Recommender system, Machine learning and Collaborative filtering are his primary areas of study.
His Artificial intelligence research is multidisciplinary, incorporating elements of Boundary and Natural language processing.
Xing Xie combines subjects such as Word, Feature learning, Information overload and Social network with his study of Information retrieval.
His work deals with themes such as Multi-task learning, Feature, Representation and Human–computer interaction, which intersect with Recommender system.
Collaborative filtering and Embedding are commonly linked in his work.
His study looks at the intersection of Embedding and topics like Variety with Set.
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