Statistical Inference for Censored Preference Data

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Ranked data arises from m raters ordering by some mechanism n items to express their preferences for the item. Such data can represent election voting, psychological and medical surveys, book and movie recommendation, and web-site ranking system such as search engines. In this proposal the investigators develop the theory and methodology of statistical inference in the case where n and m tend to infinity, and each rater provides an increasingly censored or partial preference information. Under this scenario, they demonstrate how to obtain consistent non-parametric estimators and develop efficient computational procedures for their use. Another aspect that is examined is visualizing preference data by embedding it in a low dimensional space, and designing appropriate surveys for preference data. The methodology and theory developed in this proposal should help build superior recommendations systems which are becoming increasingly popular in today's online businesses. Such systems build a customized list of recommended items based on the user's past preferences. The proposal also develops visualization techniques for such data which should increase the ability of businesses to analyze customer survey data. In the past such techniques have been either ad-hoc and lacking statistical interpretation, or computationally prohibitive. This proposal aims at developing useful tools for preference data that are both statistically interpretable and computationally efficient, in a realistic large data setting.