Trust Networks for Recommender Systems [electronic resource] / by Patricia Victor, Chris Cornelis, Martine de Cock.
By: Victor, Patricia [author.].
Contributor(s): Cornelis, Chris [author.] | de Cock, Martine [author.] | SpringerLink (Online service).
Material type:
BookSeries: Atlantis Computational Intelligence Systems: 4Publisher: Paris : Atlantis Press, 2011Description: XIV, 202 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9789491216084.Subject(s): Computer science | Logic design | Artificial intelligence | Computer Science | Artificial Intelligence (incl. Robotics) | Logic DesignDDC classification: 006.3 Online resources: Click here to access online
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Springer eBooksSummary: 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.
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.
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