Trust management is an increasingly important issue in large social networks, where the amount of data is too extensive to be analysed by ordinary users. Hence there is an urgent need for research aiming at building automated systems that can support users in making their decisions concerning trust.This work is a preliminary implementation of selected ideas described in our previous research proposal which concerns taking a machine-learning approach to the problem of trust prediction in social networks.We report experiments conducted on a publicly available social network dataset epinions.com. The results indicate that i) it is possible to predict trust to some extent, but much room for improvement is present; ii) enriching the model with attributes based on similarity between users can significantly improve trust prediction accuracy for more similar users.
['Piotr Borzymek', 'Marcin Sydow', 'Adam Wierzbicki']
Enriching Trust Prediction Model in Social Network with User Rating Similarity