Elevating Prediction Accuracy n Trust-aware Collaborative Filtering Recommenders through T-index Metric and TopTrustee Lists

Abstract

<span style="font-family: NimbusRomNo9L-Medi; font-size: 9pt; mso-fareast-font-family: SimSun; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA; mso-fareast-theme-font: minor-fareast; mso-bidi-font-family: NimbusRomNo9L-Medi;"><span style="font-family: Times New Roman;">The growing popularity of Social Networks raises the important issue of trust. Among many systems which have realized the impact of trust, Recommender Systems have been the most influential ones. Collaborative Filtering Recommenders take advantage of trust relations between users for generating more accurate predictions. In this paper, we propose a semantic recommendation framework for creating trust relationships among all types of users with respect to different types of items, which are accessed by unique URI across heterogeneous networks and environments. We gradually build up the trust relationships between users based on the rating information from user profiles and item profiles to generate trust networks of users. For analyzing the formation of trust networks, we employ Tindex as an estimate of a user’s trustworthiness to identify and select neighbors in an effective manner. In this work, we utilize 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, he/she is able to find users who can be trustworthy neighbors even 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 Tindex results in better prediction accuracy and coverage of recommendations collected along few edges that connect users on a Social Network. </span></span

Towards a Social Trust-Aware Recommender for Teachers

“…In the present context, the higher the Tindex value of a user, the more trustworthy that user becomes. Fazeli et al showed how the T-index improves the structure of a generated trust network of users by creating connections to more trustworthy users (Fazeli et al 2010). Trust networks of users are described as a graph in which nodes represent the users and directed edges show the trust relationships (Dokoohaki and Matskin 2008;Fazeli et al 2010;Golbeck 2005).…”

“…Fazeli et al showed how the T-index improves the structure of a generated trust network of users by creating connections to more trustworthy users (Fazeli et al 2010). Trust networks of users are described as a graph in which nodes represent the users and directed edges show the trust relationships (Dokoohaki and Matskin 2008;Fazeli et al 2010;Golbeck 2005). Fazeli et al created the trust relationships between users based on the ratings users gave to the items in their system (Fazeli et al 2010).…”

“…We refer to the data that comes from the social activities of users, as social data. In this research, we aim to enhance the existing T-index approach of (Fazeli et al 2010) by using social data of users. We intend to create trust relationship between users based on the collected social data from their activities within the ODS platform.…”

“…Such trustworthy resources enable teachers to feel more comfortable to share and interact within a closed and trustful community. To achieve this, we follow a trust-based recommender system proposed by Fazeli et al (2010) to create trust networks of users based on the rating information of user profile and item profile. Fazeli et al proposed a concept called Tindex to measure trustworthiness of users in order to improve the process of finding the nearest neighbours.…”

“…Trust networks of users are described as a graph in which nodes represent the users and directed edges show the trust relationships (Dokoohaki and Matskin 2008;Fazeli et al 2010;Golbeck 2005). Fazeli et al created the trust relationships between users based on the ratings users gave to the items in their system (Fazeli et al 2010). Although user evaluations in the form of ratings is one of the important examples of users' activities within a social environment, other social activities of users should not be ignored up front.…”