What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
His primary areas of investigation include Music information retrieval, Information retrieval, World Wide Web, Recommender system and Audio signal.
As a part of the same scientific family, Markus Schedl mostly works in the field of Music information retrieval, focusing on Cognitive models of information retrieval and, on occasion, Music and artificial intelligence and User modeling.
His Information retrieval study incorporates themes from Context, Web application, Web page and Active listening.
His World Wide Web research is multidisciplinary, incorporating perspectives in Pop music automation and Similarity.
His Recommender system research includes elements of Knowledge management, Social media and Big Five personality traits, Personality.
His Audio signal study combines topics from a wide range of disciplines, such as Recurrent neural network and Artificial intelligence.
His most cited work include:
- Current challenges and visions in music recommender systems research (107 citations)
- Music Information Retrieval: Recent Developments and Applications (107 citations)
- A music search engine built upon audio-based and web-based similarity measures (97 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Information retrieval, Recommender system, Multimedia, Music information retrieval and World Wide Web.
His biological study spans a wide range of topics, including Context, Web page, Similarity, Artificial intelligence and Popularity.
Markus Schedl has researched Recommender system in several fields, including Big Five personality traits, Personality, User modeling and Active listening.
His studies in Multimedia integrate themes in fields like Classical music and Music listening.
His Music information retrieval research includes elements of Human–computer information retrieval, Search engine indexing and Audio signal.
His work on Social media, Microblogging and Personalization as part of general World Wide Web study is frequently linked to Information system, bridging the gap between disciplines.
He most often published in these fields:
- Information retrieval (33.98%)
- Recommender system (32.05%)
- Multimedia (25.10%)
What were the highlights of his more recent work (between 2017-2021)?
- Recommender system (32.05%)
- Active listening (18.53%)
- Information retrieval (33.98%)
In recent papers he was focusing on the following fields of study:
Markus Schedl mainly investigates Recommender system, Active listening, Information retrieval, Collaborative filtering and Artificial intelligence.
His Recommender system research includes themes of Quality, Context, Metadata, User modeling and Popularity.
His research in Active listening intersects with topics in Applied psychology and Music information retrieval.
His Information retrieval research is multidisciplinary, incorporating perspectives in Profiling and Benchmark.
His Collaborative filtering study integrates concerns from other disciplines, such as Artificial neural network, Feature extraction and Feature vector.
His Deep learning and Ranking study in the realm of Artificial intelligence connects with subjects such as Baseline and Perspective.
Between 2017 and 2021, his most popular works were:
- Current challenges and visions in music recommender systems research (107 citations)
- Recsys challenge 2018: automatic music playlist continuation (40 citations)
- Audio-visual encoding of multimedia content for enhancing movie recommendations (24 citations)
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