What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Information retrieval
His primary areas of investigation include Information retrieval, Artificial intelligence, Relevance, Machine learning and Relevance feedback.
His work blends Information retrieval and Interface studies together.
His work in Artificial intelligence covers topics such as Natural language processing which are related to areas like Subject, Presentation and Semantic computing.
The various areas that he examines in his Relevance study include Web page, The Internet, Information needs and Set.
His work focuses on many connections between Machine learning and other disciplines, such as Hidden Markov model, that overlap with his field of interest in Image processing and Bayesian information criterion.
His biological study spans a wide range of topics, including Control, Query expansion, Web retrieval and Human–computer interaction.
His most cited work include:
- On social networks and collaborative recommendation (537 citations)
- HMM Model Selection Issues for Soccer Video (344 citations)
- How users assess web pages for information seeking (186 citations)
What are the main themes of his work throughout his whole career to date?
Information retrieval, Artificial intelligence, World Wide Web, Relevance and Data mining are his primary areas of study.
His Information retrieval research includes elements of Cluster analysis, Information needs and Relevance feedback.
The Artificial intelligence study combines topics in areas such as Pattern recognition, Natural language processing, Computer vision, Machine learning and TRECVID.
His World Wide Web course of study focuses on Multimedia and Multimedia search.
His study looks at the relationship between Relevance and topics such as Ranking, which overlap with Ranking.
His research integrates issues of Query language, Web query classification and Query optimization in his study of Query expansion.
He most often published in these fields:
- Information retrieval (52.00%)
- Artificial intelligence (24.29%)
- World Wide Web (17.14%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (24.29%)
- Machine learning (10.86%)
- Information retrieval (52.00%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Information retrieval, Recommender system and Lifelog.
In his research on the topic of Artificial intelligence, Entity linking and Semantics is strongly related with Natural language processing.
His work in the fields of Machine learning, such as Ranking, overlaps with other areas such as Factorization and Matrix decomposition.
Joemon M. Jose regularly ties together related areas like Pattern recognition in his Information retrieval studies.
The study incorporates disciplines such as Generative model, Pooling, Leverage and Convolutional neural network in addition to Recommender system.
Joemon M. Jose works mostly in the field of Data mining, limiting it down to topics relating to Relevance and, in certain cases, Measure.
Between 2015 and 2021, his most popular works were:
- A Simple Convolutional Generative Network for Next Item Recommendation (108 citations)
- LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates (69 citations)
- Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Joemon M. Jose spends much of his time researching Artificial intelligence, Machine learning, Lifelog, Information retrieval and Recommender system.
His Artificial intelligence study incorporates themes from Relevance and Integer programming.
His work on Ranking as part of general Machine learning study is frequently linked to Matrix decomposition and Popularity, bridging the gap between disciplines.
Joemon M. Jose has included themes like World Wide Web and Internet privacy in his Lifelog study.
His studies deal with areas such as Semantics and Language modelling as well as Information retrieval.
His research investigates the link between Recommender system and topics such as Leverage that cross with problems in Collaborative filtering, Feature, Artificial neural network, Embedding and Time complexity.
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