What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- The Internet
Data mining, Information retrieval, Mobile computing, Search engine indexing and Distributed computing are his primary areas of study.
His Data mining research is multidisciplinary, relying on both Spatial database, Representation, Cluster analysis, k-nearest neighbors algorithm and Collaborative filtering.
He works mostly in the field of Information retrieval, limiting it down to topics relating to World Wide Web and, in certain cases, Ubiquitous computing, as a part of the same area of interest.
His Mobile computing research also works with subjects such as
- Wireless network that connect with fields like Computer network,
- Mobile database together with Context-aware pervasive systems and Services computing.
His Search engine indexing research incorporates elements of Signature, Atomic broadcast, Relevance, Ranking and Data structure.
The concepts of his Distributed computing study are interwoven with issues in Cache algorithms, Cache invalidation, Cache, Object and Scheme.
His most cited work include:
- Exploiting geographical influence for collaborative point-of-interest recommendation (775 citations)
- Document ranking and the vector-space model (314 citations)
- Location-based spatial queries (275 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Data mining, Information retrieval, Computer network, Mobile computing and Distributed computing.
His work deals with themes such as Overhead, Theoretical computer science, Spatial database, Search engine indexing and Search algorithm, which intersect with Data mining.
As part of his studies on Information retrieval, Dik Lun Lee often connects relevant areas like Personalization.
His work in the fields of Cache and Atomic broadcast overlaps with other areas such as Dissemination and Access time.
His Mobile computing research includes themes of Scalability, Wireless network, Real-time computing, Mobile search and Optimization problem.
His Distributed computing research is multidisciplinary, incorporating elements of Object, Broadcasting and Cache invalidation.
He most often published in these fields:
- Data mining (28.07%)
- Information retrieval (26.75%)
- Computer network (14.04%)
What were the highlights of his more recent work (between 2010-2021)?
- Information retrieval (26.75%)
- Recommender system (6.14%)
- Data mining (28.07%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Information retrieval, Recommender system, Data mining, Collaborative filtering and Search engine.
His Information retrieval study integrates concerns from other disciplines, such as Ranking and Graph.
His research is interdisciplinary, bridging the disciplines of Heuristic and Data mining.
His Collaborative filtering study combines topics in areas such as Factorization, Preference and Social network.
The various areas that he examines in his Search engine study include Ranking, Relevance, Cluster analysis and Personalization.
His biological study spans a wide range of topics, including Point of interest and Naive Bayes classifier.
Between 2010 and 2021, his most popular works were:
- Exploiting geographical influence for collaborative point-of-interest recommendation (775 citations)
- IR-Tree: An Efficient Index for Geographic Document Search (199 citations)
- Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks (173 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- The Internet
Dik Lun Lee focuses on Information retrieval, Collaborative filtering, Artificial intelligence, Recommender system and Data mining.
The study incorporates disciplines such as Ranking, Point of interest, Naive Bayes classifier and Social network in addition to Information retrieval.
His research integrates issues of Ranking and Cluster analysis in his study of Ranking.
His studies deal with areas such as Biclustering, Preference, Mobile device and Identification as well as Collaborative filtering.
His Artificial intelligence research includes elements of Tree and Machine learning.
He usually deals with Data mining and limits it to topics linked to Search engine and Document clustering, Search engine indexing, Query expansion and Mobile computing.
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