Isbn: 9789491216398 - trust networks for recommender systems (atlantis computational intelligence systems, band 4) (5 Ergebnisse)

Zustand: New. In English.

Paperback. Zustand: Brand New. 2011 edition. 235 pages. 9.20x6.10x0.46 inches. In Stock.

Zustand: New. Featuring innovative contributions to the field such as a new bilattice-based model for trust and distrust, this book on a hot research topic is the first in-depth study of the potential of distrust in the emerging domain of trust-enhanced recommendation. Series: Atlantis Computational Intelligence Systems. Num Pages: 215 pages, 26 black & white tables, biography. BIC Classification: KJE; KJS; UYF; UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 13. Weight in Grams: 338. . 2013. Paperback. . . . . Books ship from the US and Ireland.…

Taschenbuch. Zustand: Neu. Trust Networks for Recommender Systems | Patricia Victor (u. a.) | Taschenbuch | Atlantis Computational Intelligence Systems | xiii | Englisch | 2013 | Atlantis Press | EAN 9789491216398 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book describes research performed in the context of trust/distrust propagation and aggregation, and their use in recommender systems. This is a hot research topic with important implications for various application areas. The main innovative contributions of the work are: -new bilattice-based model for trust and distrust, allowing for ignorance and inconsistency -proposals for various propagation and aggregation operators, including the analysis of mathematical properties -Evaluation of these operators on real data, including a discussion on the data sets and their characteristics. -A novel approach for identifying controversial items in a recommender system -An analysis on the utility of including distrust in recommender systems -Various approaches for trust based recommendations (a.o. base on collaborative filtering), an in depth experimental analysis, and proposal for a hybrid approach -Analysis of various user types in recommender systems to optimize bootstrapping of cold start users.…