Physical Sciences and Mathematics Commons^™

Estimating Error And Bias Of Offline Recommender System Evaluation Results, Mucun Tian

Boise State University Theses and Dissertations

Recommender systems are software applications deployed on the Internet to help people find useful items (e.g. movies, books, music, products) by providing recommendation lists. Before deploying recommender systems online, researchers and practitioners generally conduct offline evaluations to compare the accuracy of top- recommendation lists among candidate algorithms using users’ history consumption data. These offline evaluations typically use metrics and methodologies borrowed from machine learning and information retrieval and have several well-known biases that affect the validity of their results, including popularity bias and other biases arising from the missing-not-at-random nature of the data used. The existence of these biases is …