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Author Zunping Cheng ♦ Hurley, N.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Robustness ♦ Prediction algorithms ♦ Mathematical model ♦ Predictive models ♦ Recommender systems ♦ Stochastic processes ♦ Accuracy ♦ least trimmed squares ♦ recommender system ♦ matrix factorization ♦ robust
Abstract Collaborative filtering (CF) recommender systems help people discover what they really need in a large set of alternatives by analyzing the preferences of other related users. Recent research has shown that the accuracy of recommendations can be improved significantly by using matrix factorization (MF) models. In particular, a mixed MF model was used by BellKor's Pragmatic Chaos to win the Netflix Prize. On the other hand, system designers must also be concerned about system robustness - the ability of the system to provide good recommendations when the system database is contaminated with some portion of noisy or erroneous data, perhaps maliciously entered by `profile injection' attackers intent on distorting system recommendations. In this paper, we focus on the robustness of MF based CF algorithms (MFCF), which usually transform the prediction of user preferences on items into a least squares problem, solved by gradient descent. As least squares is known to be sensitive to outliers, it is not surprising that MF algorithms are vulnerable to attack. Nevertheless a number of `robust statistics' have been proposed since the 1960's that provide alternative data fitting strategies that are less sensitive to outliers. In particular, in this paper, we propose a least trimmed squares based MF (LTSMF) to help improve the robustness of the least squares based MF (LSMF) models. Least trimmed squares is shown to be more robust than least squares and another popular robust method - M-estimator. Experiments also show that LTSMF outperforms previous robust CF models on both accuracy and robustness.
ISBN 9781424488179
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-10-27
Publisher Place France
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 299.40 kB
Page Count 8
Starting Page 105
Ending Page 112


Source: IEEE Xplore Digital Library