Lyle H. Ungar

TL;DR: In this article, the authors proposed a system that automatically constructs a target profile for each target object in the electronic media based on the frequency with which each word appears in an article relative to its overall frequency of use in all articles, as well as a "target profile interest summary" for each user.

TL;DR: A method for recommending items that combines content and collaborative data under a single probabilistic framework is developed, and it is demonstrated empirically that the various components of the testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems.

TL;DR: In this paper, a system and method for scheduling the receipt of desired movies and other forms of data from a network which simultaneously distributes many sources of such data to many customers, as in a cable television system, is presented.

TL;DR: This represents the largest study, by an order of magnitude, of language and personality, and found striking variations in language with personality, gender, and age.

TL;DR: This work presents a new technique for clustering large datasets, using a cheap, approximate distance measure to eciently divide the data into overlapping subsets the authors call canopies, and presents ex- perimental results on grouping bibliographic citations from the reference sections of research papers.