What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Martha Larson spends much of her time researching Information retrieval, Collaborative filtering, Artificial intelligence, Recommender system and Data mining.
Her Information retrieval research incorporates elements of Metadata, World Wide Web and Social network.
Her research investigates the connection between Collaborative filtering and topics such as Context that intersect with issues in Similarity measure.
Her studies in Artificial intelligence integrate themes in fields like Machine learning, Multimedia and Natural language processing.
Her research integrates issues of Novelty and Bayesian probability in her study of Recommender system.
Her studies deal with areas such as Ranking, Leverage and Automatic summarization as well as Data mining.
Her most cited work include:
- Collaborative Filtering beyond the User-Item Matrix: A Survey of the State of the Art and Future Challenges (520 citations)
- CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering (230 citations)
- List-wise learning to rank with matrix factorization for collaborative filtering (193 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Artificial intelligence, Information retrieval, Multimedia, World Wide Web and Recommender system.
Her Artificial intelligence research integrates issues from Natural language processing, Speech recognition, Ranking, Machine learning and Pattern recognition.
Her work on Query expansion, Search engine and Ranking as part of her general Information retrieval study is frequently connected to Coherence, thereby bridging the divide between different branches of science.
Martha Larson interconnects Field, Speech analytics, Social media and Conjunction in the investigation of issues within Multimedia.
In the subject of general World Wide Web, her work in Crowdsourcing, Information needs and The Internet is often linked to Benchmarking and Engineering, thereby combining diverse domains of study.
She works in the field of Recommender system, namely Collaborative filtering.
She most often published in these fields:
- Artificial intelligence (30.77%)
- Information retrieval (27.27%)
- Multimedia (28.32%)
What were the highlights of her more recent work (between 2018-2021)?
- Artificial intelligence (30.77%)
- Adversarial system (3.85%)
- Recommender system (21.68%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Artificial intelligence, Adversarial system, Recommender system, Information retrieval and Multimedia.
Her research in Artificial intelligence intersects with topics in Machine learning, Computer vision and Pattern recognition.
Her Recommender system research is multidisciplinary, incorporating perspectives in Data mining, Moderation, Ranking, Harm and Internet privacy.
Her Data mining research is multidisciplinary, incorporating elements of Collaborative filtering, Bayesian probability and Synthetic data.
Martha Larson combines subjects such as Plot, Narrative and Social network with her study of Information retrieval.
Martha Larson has included themes like Column and Personally identifiable information in her Multimedia study.
Between 2018 and 2021, her most popular works were:
- Towards Large Yet Imperceptible Adversarial Image Perturbations With Perceptual Color Distance (21 citations)
- Who's Afraid of Adversarial Queries?: The Impact of Image Modifications on Content-based Image Retrieval (14 citations)
- Top-N Recommendation with Multi-Channel Positive Feedback using Factorization Machines (10 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Adversarial system, Computer vision, Information retrieval and Artificial neural network.
Her work in the fields of Artificial intelligence, such as Color vision, intersects with other areas such as Transformation.
Her biological study spans a wide range of topics, including RGB color model, Visualization and Pattern recognition.
In general Computer vision study, her work on Pixel and Image often relates to the realm of Filter and Color gel, thereby connecting several areas of interest.
Her studies deal with areas such as Narrative and Social network as well as Information retrieval.
Her Artificial neural network research is multidisciplinary, incorporating perspectives in Manner of articulation, Class, Consonant, Vowel and Representation.
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