H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 43 Citations 6,292 131 World Ranking 3940 National Ranking 93

Overview

What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Machine learning
  • The Internet

The scientist’s investigation covers issues in Recommender system, Human–computer interaction, Knowledge management, Decision support system and Preference elicitation. Many of her studies on Recommender system apply to Personality as well. Her studies in Human–computer interaction integrate themes in fields like World Wide Web and Product.

Her Knowledge management research incorporates elements of User experience design, Cognitive effort and Internet privacy. Her biological study deals with issues like Consistency, which deal with fields such as Quality and Usability. Her work carried out in the field of Decision support system brings together such families of science as Range, Outcome and Order.

Her most cited work include:

  • A user-centric evaluation framework for recommender systems (431 citations)
  • Trust building with explanation interfaces (211 citations)
  • Evaluating recommender systems from the user's perspective: survey of the state of the art (191 citations)

What are the main themes of her work throughout her whole career to date?

Pearl Pu spends much of her time researching Recommender system, Human–computer interaction, World Wide Web, Artificial intelligence and Knowledge management. In her works, she conducts interdisciplinary research on Recommender system and Preference elicitation. Her work deals with themes such as E-commerce, Decision support system, Dialog box and Set, which intersect with Human–computer interaction.

Her work in World Wide Web is not limited to one particular discipline; it also encompasses User modeling. Her Artificial intelligence research is multidisciplinary, relying on both Machine learning, Computer vision and Natural language processing. Many of her studies involve connections with topics such as Quality and Usability.

She most often published in these fields:

  • Recommender system (30.11%)
  • Human–computer interaction (30.11%)
  • World Wide Web (18.82%)

What were the highlights of her more recent work (between 2014-2021)?

  • Behavior change (3.23%)
  • Human–computer interaction (30.11%)
  • Artificial intelligence (15.59%)

In recent papers she was focusing on the following fields of study:

Pearl Pu focuses on Behavior change, Human–computer interaction, Artificial intelligence, Applied psychology and Dialog box. Pearl Pu integrates Human–computer interaction with Constructive in her research. The concepts of her Artificial intelligence study are interwoven with issues in Machine learning and Natural language processing.

Her study in Dialog box is interdisciplinary in nature, drawing from both Quality and Natural language. Her Natural language research includes elements of Surprise and World Wide Web. Her Set research incorporates elements of Structure and Eye movement.

Between 2014 and 2021, her most popular works were:

  • Trust Building in Recommender Agents (46 citations)
  • Dystemo: Distant Supervision Method for Multi-Category Emotion Recognition in Tweets (10 citations)
  • Exploring physical activity monitoring devices for diabetic and obese patients (10 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • The Internet

Pearl Pu mainly focuses on Internet privacy, Gerontology, Strong ties, Behavior change and Competition. Her research integrates issues of Multimedia and Competence in her study of Internet privacy. Her Physical exercise research extends to the thematically linked field of Gerontology.

Her Strong ties research includes elements of Social relationship, Baseline, Social psychology, Work and Online community. Many of her studies on Behavior change involve topics that are commonly interrelated, such as Set.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

A user-centric evaluation framework for recommender systems

Pearl Pu;Li Chen;Rong Hu.
conference on recommender systems (2011)

688 Citations

Evaluating recommender systems from the user's perspective: survey of the state of the art

Pearl Pu;Li Chen;Rong Hu.
User Modeling and User-adapted Interaction (2012)

302 Citations

Trust building with explanation interfaces

Pearl Pu;Li Chen.
intelligent user interfaces (2006)

279 Citations

Survey of Preference Elicitation Methods

Li Chen;Pearl Pu.
(2004)

226 Citations

Critiquing-based recommenders: survey and emerging trends

Li Chen;Pearl Pu.
User Modeling and User-adapted Interaction (2012)

225 Citations

Trust-inspiring explanation interfaces for recommender systems

Pearl Pu;Li Chen.
Knowledge Based Systems (2007)

212 Citations

Issues and Applications of Case Based Reasoning to Design

Mary Lou Maher;Pearl Pu.
Issues and Applications of Case Based Reasoning to Design (1997)

187 Citations

Enhancing collaborative filtering systems with personality information

Rong Hu;Pearl Pu.
conference on recommender systems (2011)

177 Citations

HealthyTogether: exploring social incentives for mobile fitness applications

Yu Chen;Pearl Pu.
Proceedings of the Second International Symposium of Chinese CHI on (2014)

175 Citations

Acceptance issues of personality-based recommender systems

Rong Hu;Pearl Pu.
conference on recommender systems (2009)

132 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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Contact us

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