There has been recent interest in collecting user or assessor preferences, rather than absolute judgments of relevance, for the evaluation or learning of ranking algorithms. Since...
We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes th...
Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in rec...
In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the...
We present a method for automated topic suggestion. Given a plain-text input document, our algorithm produces a ranking of novel topics that could enrich the input document in a m...