What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Information retrieval
Ji-Rong Wen spends much of his time researching Information retrieval, Web search query, Web page, Data mining and Ranking.
His Information retrieval research incorporates elements of Static web page, Cluster analysis and Web modeling.
His Web search query research is multidisciplinary, relying on both Query expansion, Multi dimensional search and Semantic search.
His research integrates issues of Search engine and Relevance feedback in his study of Query expansion.
In his study, Visualization, Hierarchical clustering, Graph and Segmentation is inextricably linked to Graph, which falls within the broad field of Web page.
His Data mining research incorporates themes from Machine learning, Data type and Information extraction, Artificial intelligence.
His most cited work include:
- VIPS: a Vision-based Page Segmentation Algorithm (484 citations)
- A large-scale evaluation and analysis of personalized search strategies (475 citations)
- Query Clustering Using User Logs (440 citations)
What are the main themes of his work throughout his whole career to date?
Ji-Rong Wen focuses on Information retrieval, Artificial intelligence, Web page, Ranking and Machine learning.
His research combines Ranking and Information retrieval.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Pattern recognition, Graph and Natural language processing.
His research in Web page intersects with topics in Link analysis and Cluster analysis.
His research on Ranking also deals with topics like
- Data mining and related Information extraction,
- Personalized search that intertwine with fields like User profile.
In his study, which falls under the umbrella issue of Web query classification, Sargable is strongly linked to Query optimization.
He most often published in these fields:
- Information retrieval (47.45%)
- Artificial intelligence (35.04%)
- Web page (17.88%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (35.04%)
- Information retrieval (47.45%)
- Machine learning (16.06%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Information retrieval, Machine learning, Personalized search and Ranking.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Natural language processing, Graph and Pattern recognition.
His work carried out in the field of Information retrieval brings together such families of science as Natural language, Focus and Information needs.
His studies deal with areas such as Domain, Collaborative learning, Adversarial system and Meta learning as well as Machine learning.
His research ties Ranking and Ranking together.
While the research belongs to areas of Ranking, he spends his time largely on the problem of Personalization, intersecting his research to questions surrounding Search engine.
Between 2019 and 2021, his most popular works were:
- Counterfactual VQA: A Cause-Effect Look at Language Bias (21 citations)
- S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization (15 citations)
- SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
Artificial intelligence, Machine learning, Information retrieval, Ranking and Domain are his primary areas of study.
The concepts of his Artificial intelligence study are interwoven with issues in Pattern recognition, Graph and Natural language processing.
Many of his research projects under Machine learning are closely connected to Generator with Generator, tying the diverse disciplines of science together.
His work on Learning to rank as part of his general Information retrieval study is frequently connected to Invariant, thereby bridging the divide between different branches of science.
His work in Ranking tackles topics such as Ranking which are related to areas like Personalized search.
He combines subjects such as Class, Selection and Shot with his study of Domain.
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