What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
Recommender system, Artificial intelligence, Machine learning, Matrix decomposition and Collaborative filtering are his primary areas of study.
While working in this field, he studies both Artificial intelligence and Noun phrase.
His studies in Machine learning integrate themes in fields like Classifier and Data mining.
The study incorporates disciplines such as Contrast and PageRank in addition to Data mining.
His Collaborative filtering research includes elements of Field, Scalability, Database and Data set.
His research investigates the connection between Bayesian probability and topics such as Stochastic gradient descent that intersect with problems in Ranking and Ranking SVM.
His most cited work include:
- BPR: Bayesian personalized ranking from implicit feedback (2500 citations)
- Factorizing personalized Markov chains for next-basket recommendation (822 citations)
- Pairwise interaction tensor factorization for personalized tag recommendation (556 citations)
What are the main themes of his work throughout his whole career to date?
Lars Schmidt-Thieme mainly focuses on Artificial intelligence, Machine learning, Recommender system, Data mining and Matrix decomposition.
His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with issues in Time series classification.
His Machine learning research includes themes of Classifier and Bayesian probability.
Lars Schmidt-Thieme combines subjects such as Information overload and Personalization with his study of Recommender system.
His research in Data mining intersects with topics in Cluster analysis, Data set and Synthetic data.
Lars Schmidt-Thieme undertakes multidisciplinary studies into Matrix decomposition and Ranking in his work.
He most often published in these fields:
- Artificial intelligence (58.27%)
- Machine learning (42.52%)
- Recommender system (27.56%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (58.27%)
- Machine learning (42.52%)
- Deep learning (3.15%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Machine learning, Deep learning, Convolutional neural network and Recommender system.
His Artificial intelligence research incorporates elements of Multivariate statistics and Pattern recognition.
Many of his studies involve connections with topics such as Data set and Machine learning.
His research integrates issues of Social media, Data-driven and Information retrieval in his study of Convolutional neural network.
His Recommender system study incorporates themes from Document classification, Data mining and Benchmark.
His biological study spans a wide range of topics, including Cluster analysis and Geolocation.
Between 2016 and 2021, his most popular works were:
- Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (35 citations)
- Personalized Deep Learning for Tag Recommendation (26 citations)
- Personalized Tag Recommendation for Images Using Deep Transfer Learning (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
His scientific interests lie mostly in Artificial intelligence, Machine learning, Artificial neural network, Information retrieval and Hyperparameter optimization.
His Artificial intelligence study frequently draws connections between adjacent fields such as Pattern recognition.
His work on Supervised learning and Self supervised learning as part of his general Machine learning study is frequently connected to Initialization, Parametric model and Node, thereby bridging the divide between different branches of science.
In the subject of general Information retrieval, his work in Recommender system and Collaborative filtering is often linked to Nonlinear system, thereby combining diverse domains of study.
Lars Schmidt-Thieme has included themes like Data set and Bipartite graph in his Recommender system study.
His Hyperparameter optimization research is multidisciplinary, relying on both Similarity learning, Domain knowledge, Surrogate model and Hyperparameter.
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