INFERRING TRUST BASED ON HIGHLY TRUSTED USERS IN A RECOMMENDER SYSTEM
Author(s):
Yussuf Abu-shaaban
Paper abstract: Trust information is becoming essential to support users making decisions when interacting e-commerce
services and social networks. Such trust information is usually represented by a directed network of
nodes (users) connected by trust edges. Trust propagation and aggregation techniques are usually used to
infer the trust value between two users with no direct trust link. In this paper, we propose and investigate
a new approach to infer trust by using recommender system information. To determine the trust value of
a user, ratings assigned by the highest trusted users to recommendations issued by that user are
aggregated to infer trust. It was found that basing trust decisions on recommendation ratings made by the
highest trusted users can achieve high accuracy outperforming less trusted users. In addition, trust value
inference based on recommendation ratings of the highest trusted users tend to give a trust value greater
than the mean of the direct trust values in a network of trust. This tendency can also be noticed, with a
smaller degree, when trust is predicted based on recommendation ratings made by less trusted users. The
work presented in this paper is the first to use recommender systems to predict trust and to analyse the
accuracy and tendencies of their use in the trust inference process.
