Rin Popescul

Categories and Subject Descriptors (2008)

Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

Probability And Statistics—Contingency table analysis We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We...

CROC: A New Evaluation Criterion for Recommender Systems 1 (2008)

Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

Evaluation of a recommender system algorithm is a challenging task due to the many possible scenarios in which such systems may be deployed. We have designed a new performance plot called the CROC...

Dynamic Feature Generation for Relational Learning (2008)

Rin Popescul, Lyle H. Ungar

Abstract. We provide a methodology which integrates dynamic feature generation from relational databases with statistical feature selection and modeling. Unlike the standard breadth- or depth-first...

In Multi-Relational Data Mining Workshop at KDD-2003. Structural Logistic Regression for Link Analysis (2008)

Rin Popescul, Lyle H. Ungar

Abstract. We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in...

Dynamic Feature Generation for Relational Learning (2008)

Rin Popescul, Lyle H. Ungar

Abstract. We provide a methodology which integrates dynamic feature generation from relational databases with statistical feature selection and modeling. Unlike the standard breadth- or depth-first...

Probability And Statistics—Contingency table analysis (2008)

Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naïve Bayes classifier on...

CROC: A New Evaluation Criterion for Recommender Systems (2008)

Andrew Schein Alexandrin, Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

Evaluation of a recommender system algorithm is a challenging task due to the many possible scenarios in which such systems may be deployed. We have designed a new performance plot called the CROC...

Categories and Subject Descriptors (2007)

Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

]: Probability And Statistics---Contingency table analysis We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We...

]: Probability And Statistics Contin.qency table anal- (2007)

Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

ysis Ve have developed a method for recommending items that combines content and collaborative data under a single probabifistic framework. We benchmark our algorithm against a naYve Bayes classifier...

1 A (Very Brief) Introduction to the Two-Way Aspect Model (2007)

Andrew I. Schein, Rin Popescul, Lyle H. Ungar

The two-way aspect model is a latent class statistical mixture model for performing soft clustering of co-occurrence data observations. It acts on data such as document/word pairs (words occurring in...

In Multi-Relational Data Mining Workshop at KDD-2003. Structural Logistic Regression for Link Analysis (2007)

Rin Popescul, Lyle H. Ungar

Abstract. We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in...

Structural Logistic Regression for Link Analysis (2003)

Alexandrin Popescul, Rin Popescul, Lyle H. Ungar

We present Structural Logistic Regression, an extension of logistic regression to modeling relational data. It is an integrated approach to building regression models from data stored in relational...

Statistical Relational Learning for Link Prediction (2003)

Alexandrin Popescul, Rin Popescul, Lyle H. Ungar

Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the...

Methods and metrics for cold-start recommendations (2002)

Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a nave Bayes classifier on...

Towards structural logistic regression: Combining relational and statistical learning (2002)

Rin Popescul, Lyle H. Ungar, Steve Lawrence, David M. Pennock

Abstract. Inductive logic programming (ILP) techniques are useful for analyzing data in multi-table relational databases. Learned rules can potentially discover relationships that are not obvious in...

Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments (2001)

Rin Popescul, Lyle H. Ungar, David M. Pennock, Steve Lawrence

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid...

Generative models for cold-start recommendations (2001)

Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

Systems for automatically recommending items (e.g., movies, products, or information) to users are becoming increasingly important in e-commerce applications, digital libraries, and other domains...

Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments (2001)

Rin Popescul, Lyle H. Ungar, David M. Pennock, Steve Lawrence

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid...

Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments (2001)

Rin Popescul, Lyle H. Ungar, David M. Pennock, Steve Lawrence

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid...

Generative Models for Cold-Start Recommendations (2001)

Andrew Schein Alexandrin, Andrew I. Schein, Rin Popescul, Lyle H. Ungar, David M. Pennock

Systems for automatically recommending items (e.g., movies, products, or information) to users are becoming increasingly important in e-commerce applications, digital libraries, and other domains...

Generative Models for Cold-Start Recommendations (2001)

Andrew Schein Alexandrin, I. Schein, Rin Popescul, David M. Pennock, Lyle H. Ungar

Systems for automatically recommending items (e.g., movies, products, or information) to users are becoming increasingly important in e-commerce applications, digital libraries, and other domains...

Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments (2001)

Rin Popescul, Lyle H. Ungar

Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid...

Clustering and identifying temporal trends in document databases (2000)

Rin Popescul, Gary William Flake, Steve Lawrence, Lyle H. Ungar, C. Lee Giles

popescul,ungar¥ We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-linked document databases. Our method can scale to large datasets because it...

Clustering and identifying temporal trends in document databases (2000)

Rin Popescul, Gary William Flake, Steve Lawrence, Lyle H. Ungar, C. Lee Giles

We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-linked document databases. Our method can scale to large datasets because it exploits the underlying...

Clustering and identifying temporal trends in document databases (2000)

C. Lee, Giles Clustering, Identifying Temporal, Rin Popescul, Rin Popescul, Gary William Flake, ...

We introduce a simple and efficient method for clustering and identifying temporal trends in hyper-linked document databases. Our method can scale to large datasets because it exploits the underlying...