H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 34 Citations 13,184 74 World Ranking 6105 National Ranking 2938

Research.com Recognitions

Awards & Achievements

2006 - ACM Senior Member

Overview

What is he best known for?

The fields of study he is best known for:

  • The Internet
  • Statistics
  • Artificial intelligence

His main research concerns Information retrieval, The Internet, Machine learning, Artificial intelligence and Recommender system. His research in Information retrieval intersects with topics in Ranking, Sentiment analysis, Common value auction and Web page. His work in Machine learning addresses subjects such as Probabilistic logic, which are connected to disciplines such as User profile and Hybrid system.

His work on Leverage and Mixture model as part of general Artificial intelligence study is frequently connected to Voting and Overfitting, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. In his articles, David M. Pennock combines various disciplines, including Recommender system and Function. His MovieLens study in the realm of Collaborative filtering interacts with subjects such as Naive Bayes classifier.

His most cited work include:

  • Mining the peanut gallery: opinion extraction and semantic classification of product reviews (1727 citations)
  • Methods and metrics for cold-start recommendations (1376 citations)
  • Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach (478 citations)

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

David M. Pennock mostly deals with Information retrieval, World Wide Web, Artificial intelligence, Collaborative filtering and Machine learning. His Information retrieval research is multidisciplinary, incorporating elements of Ranking, Web page and User profile. His study in the fields of Online advertising and Citation under the domain of World Wide Web overlaps with other disciplines such as Persistence.

His Artificial intelligence research is multidisciplinary, incorporating perspectives in Computational complexity theory and Pattern recognition. His Collaborative filtering research is under the purview of Recommender system. David M. Pennock has researched Recommender system in several fields, including Mixture model, Probabilistic logic and Quality.

He most often published in these fields:

  • Information retrieval (23.33%)
  • World Wide Web (19.17%)
  • Artificial intelligence (16.67%)

What were the highlights of his more recent work (between 2008-2020)?

  • Econometrics (6.67%)
  • Market maker (5.83%)
  • Mathematical economics (6.67%)

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

David M. Pennock focuses on Econometrics, Market maker, Mathematical economics, Bounded function and Market liquidity. The concepts of his Econometrics study are interwoven with issues in Entropy, Baseline, Data mining and Search engine. His Bounded function study incorporates themes from Upper and lower bounds, Hyperbolic absolute risk aversion and Scoring rule.

His Market liquidity research includes themes of Price discovery and Microeconomics, Market price. His study in Prediction market is interdisciplinary in nature, drawing from both Scheme, Statistical model and Mean squared error. His Probabilistic logic study combines topics in areas such as Collaborative filtering, Web page, Information retrieval and User profile.

Between 2008 and 2020, his most popular works were:

  • Predicting consumer behavior with Web search (458 citations)
  • Prediction without markets (53 citations)
  • Computational challenges in e-commerce (29 citations)

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

  • Statistics
  • The Internet
  • Artificial intelligence

Generalization, Mathematical economics, Econometrics, Prediction market and Knowledge management are his primary areas of study. Generalization is intertwined with Bounded function, Greedy algorithm, Value, Valuation and Theoretical computer science in his study. His Mathematical economics research integrates issues from Market liquidity, Constraint generation and Scheme.

The Econometrics study combines topics in areas such as Baseline, Data mining, Chart, Search engine and Rank. His work carried out in the field of Prediction market brings together such families of science as Calibration, Diminishing returns and Complete information. David M. Pennock interconnects E-commerce and The Internet in the investigation of issues within Knowledge management.

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

Mining the peanut gallery: opinion extraction and semantic classification of product reviews

Kushal Dave;Steve Lawrence;David M. Pennock.
the web conference (2003)

2991 Citations

Methods and metrics for cold-start recommendations

Andrew I. Schein;Alexandrin Popescul;Lyle H. Ungar;David M. Pennock.
international acm sigir conference on research and development in information retrieval (2002)

2184 Citations

Predicting consumer behavior with Web search

Sharad Goel;Jake M. Hofman;Sébastien Lahaie;David M. Pennock.
Proceedings of the National Academy of Sciences of the United States of America (2010)

754 Citations

Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach

David M. Pennock;Eric Horvitz;Steve Lawrence;C. Lee Giles.
uncertainty in artificial intelligence (2000)

704 Citations

Using internet searches for influenza surveillance.

Philip M. Polgreen;Yiling Chen;David M. Pennock;Forrest D. Nelson.
Clinical Infectious Diseases (2008)

697 Citations

Winners don't take all: Characterizing the competition for links on the web

David M. Pennock;Gary William Flake;Steve Lawrence;Eric J. Glover.
Proceedings of the National Academy of Sciences of the United States of America (2002)

626 Citations

Winner''s Don''t Take All

David Pennock;Gary William Flake;Steve Lawrence;Eric Glover.
Proceedings of the National Academy of Sciences of the United States of America (2002)

623 Citations

Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments

Alexandrin Popescul;Lyle H. Ungar;David M. Pennock;Steve Lawrence.
uncertainty in artificial intelligence (2001)

579 Citations

Prediction Markets: Does Money Matter?

Emile Servan-Schreiber;Justin Wolfers;David M. Pennock;Brian Galebach.
Electronic Markets (2004)

428 Citations

Using web structure for classifying and describing web pages

Eric J. Glover;Kostas Tsioutsiouliklis;Steve Lawrence;David M. Pennock.
the web conference (2002)

426 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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