SWT Spring2011 Project Idea

Social Networks have dominated growth and popularity of the Web to an extent

which has never been witnessed before. Web-Based Social Networks attract millions

of users every day. Introduction of Reputation-based Trust grounded on the Web

of Semantics, created an opportunity to show how Social Networks can create value

for Web-based systems. Collaborative Filtering Recommenders have been among

many systems which have begun taking full advantage of Social Trust phenomena

for generating more accurate predictions. In this work, we propose a semantic recommendation

framework for creating trust relationships among all types of users with

respect to different types of items, accessed by unique URI across heterogeneous

networks and environments. We gradually build up the trust relationships between

users based on the rating information of user profile and item profile to generate trust

networks of users. For analyzing the evolution of constructed networks of trust, we

utilize T-index as an estimate of a user’s trustworthiness to identify and select neighbors

in an effective manner. In this work, we employ T-index to form the list of

an item’s raters, called TopTrustee list for keeping the most reliable users who have

already shown interest in the respective item. Thus, when a user rates an item, it

is able to find users who can be trustworthy neighbors though they might not be

accessible within an upper bound of traversal path length. An empirical evaluation

demonstrates how T-index improves the Trust Network structure by generating connections

to more trustworthy users. We also show that exploiting T-index results

in better prediction accuracy and coverage of recommendations collected along few

edges that connect users on a Social Network.