Historical Event Detection

Historians and political scientists commonly read large quantities of text to construct an accurate picture of significant events. Our methods help historians identify possible events from the texts of historical communication.

Detecting and Characterizing Events
EMNLP, 2016.

Who, What, When, Where, and Why? A Computational Approach to Understanding Historical Events Using State Department Cables
Text As Data, 2015. (slides)

Social Poisson Factorization

The downside to most algorithmic recommendations is that, for some people, part of the appeal of reading, watching, or consuming other media is in creating shared experiences with friends. We incorporate the ratings of friends (and not just friends' general preferences) in providing personalized recommendations.

A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
RecSys, 2015. (slides)

SPF project page (includes code) or SPF code directly on GitHub



A Probabilistic Model for Using Social Networks in Personalized Item Recommendation
Invited Talk at BYU, 2015.

Poisson Trust Factorization for Incorporating Social Networks into Personalized Item Recommendation
NIPS Workshop: What Difference Does Personalization Make?, 2013.

Poisson Trust Factorization for Incorporating Social Networks into Personalized Item Recommendation
General Exam presentation, 2013.

Recommendations for Groups

We performed a large-scale study of television viewing habits, focusing on how individuals adapt their preferences when consuming content with others. We constructed a simple model for estimating how individual preferences are combined in group settings.

A Large-scale Exploration of Group Viewing Patterns
TVX (ACM International Conference on Interactive Experiences for Tekevision and Online Video), 2014.

Mining Large-scale TV Group Viewing Patterns for Group Recommendation
Microsoft Reseach Technical Report, 2013.

Visualizing Topic Models

Topic modeling is a machine learning method that learns underlying themes in a collection of documents, which can be used to summarize and organize the documents. We have created a method for visualizing topic models, allowing users to explore a corpus by navigating between high level topic descriptions and individual documents, hopefully deepening their understanding of the corpus.

Visualizing Topic Models
International AAAI Conference on Social Media and Weblogs, 2012.

Original TMVE code

Online TMV code

Wikipedia Demo