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Computer Science - Artificial Intelligence | Trust Networks for Recommender Systems

Trust Networks for Recommender Systems

Victor, Patricia, Cornelis, Chris, De Cock, Martine

1st Edition., XIII, 202 p.

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  • Includes the first in-depth investigation of the potential of distrust in the newly emerging domain of trust-enhanced recommendation
  • Puts the budding field of trust-enhanced recommender systems more prominently on the map
  • Tackles topical problems in the recommender systems domain such as the identification and recommendations of controversial items, and the mitigation of the cold start problem by offering connection guidance to new 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.

Content Level » Research

Keywords » Artificial Intelligence - Fuzzy Logic - Recommender Systems - Trust Networks

Related subjects » Artificial Intelligence - Hardware

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