"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying formerly unused machine learning methodologies, such as supervised learning. The application of various learning algorithms, such as linear regression, matrix decomposition, and decision trees, illustrates how to translate the trust modeling problem into a (supervised) learning problem. The book also shows how novel machine learning techniques can improve the accuracy of trust assessment compared to…
Counted ratings come from accounts older than 7 days and were made on or after publication. Finished readers weigh ×1.0. Readers who have not finished yet weigh ×0.5. Did-not-finish ratings are shown but not counted.
Editions
5 editions
Other · English · 2020
Taylor & Francis Group · 9780367739331
Other · English · 2014
Taylor & Francis Group · 232 pages · 9780429159480
Other · English · 2014
Taylor & Francis Group · 232 pages · 9781482226676
Other · English · 2014
Taylor & Francis · 208 pages · 9781482226669
Other · English · 2014
Taylor & Francis Group · 227 pages · 9781322637464