@inproceedings{20af2f2fcdca431da3cd1d98a1592e31,
title = "Cleaning up toxic waste: Removing nefarious contributions to recommendation systems",
abstract = "Recommendation systems are becoming increasingly important, as evidenced by the popularity of the Netflix prize and the sophistication of various online shopping systems. With this increase in interest, a new problem of nefarious or false rankings that compromise a recommendation system's integrity has surfaced. We consider such purposefully erroneous rankings to be a form of 'toxic waste,' corrupting the performance of the underlying algorithm. In this paper, we propose an adaptive reweighted algorithm as a possible approach towards correcting this problem. Our algorithm relies on finding a low-rank-plus-sparse decomposition of the recommendation matrix, where the adaptation of the weights aids in rejecting the malicious contributions. Simulations suggest that our algorithm converges fairly rapidly and produces accurate results.",
keywords = "Adaptive optimization, convergence, sparsity, toxic waste",
author = "Adam Charles and Ali Ahmed and Aditya Joshi and Stephen Conover and Christopher Turnes and Mark Davenport",
year = "2013",
month = oct,
day = "18",
doi = "10.1109/ICASSP.2013.6638932",
language = "English (US)",
isbn = "9781479903566",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "6571--6575",
booktitle = "2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings",
note = "2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference date: 26-05-2013 Through 31-05-2013",
}