Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2006-03-23
Physics
Condensed Matter
Disordered Systems and Neural Networks
19 pages, 5 figures, accepted for publication in Physica A
Scientific paper
10.1016/j.physa.2006.04.057
Advances in information technology reduce barriers to information propagation, but at the same time they also induce the information overload problem. For the making of various decisions, mere digestion of the relevant information has become a daunting task due to the massive amount of information available. This information, such as that generated by evaluation systems developed by various web sites, is in general useful but may be noisy and may also contain biased entries. In this study, we establish a framework to systematically tackle the challenging problem of information decoding in the presence of massive and redundant data. When applied to a voting system, our method simultaneously ranks the raters and the ratees using only the evaluation data, consisting of an array of scores each of which represents the rating of a ratee by a rater. Not only is our appraoch effective in decoding information, it is also shown to be robust against various hypothetical types of noise as well as intentional abuses.
Laureti Paolo
Moret Lionel
Yu Yi-Kuo
Zhang Yi-Cheng
No associations
LandOfFree
Decoding Information from noisy, redundant, and intentionally-distorted sources does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Decoding Information from noisy, redundant, and intentionally-distorted sources, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Decoding Information from noisy, redundant, and intentionally-distorted sources will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-305539