Trusting the Trusted: An Empirical Analysis

Abstract

INTRODUCTION The approach used for solving the cold start problem in a trust-aware recommender system is to ask a cold start user to select few existing users in the system that they trust. Using the ratings of the items rated by the selected trusted users, recommendations are generated for the cold start user. An implicit assumption made in this solution is that cold start users or users new to the system have knowledge of the existing users in the system. Moreover, even if the cold start user has knowledge of the existing users, it is assumed that he is aware of the ratings given by the existing users to different items. To overcome the shortcomings of the proposed approach, recent research have started focusing on the problem: to whom should the new users connect to? (Victor, Cornelis, De Cock, & Teredesai, 2008). The suggested approach is to identify key figures in the trust network based on their impact on coverage and accuracy of recommendations made and then ask the cold start user to choose few of these users presented before them as trusted users. In this paper, we analyze the trust connections of the cold start users in the dataset from Massa and Avesani (2006b), a real life recommender system dataset, to examine What are the characteristics of those users that cold start users actually trust? The objective of our work is to provide insights on the type of users that should be preferred while creating a list of prospective trusted users to be presented before a cold start user. Our paper first discusses the existing parameters that form the basis of selecting prospective trusted users and the reasoning behind it. We then analyze the set of users trusted by cold start users using four parameters: number of outgoing links, number of incoming links, number of items rated, and a hybrid parameter defined as preference score. Using rank correlation coefficient as the metric we show that in the Epinions dataset (Massa & Avesani, 2006a), users with higher number of incoming links are preferred as trusted users as compared to other three parameters. We also examine which parameter gives the best predictive accuracy when selected as trusted user. Our results show that those cold start users that trust users with high preference scores have the benefit of getting more accurate recommendations. TRUST IN WEB BASED SOCIAL NETWORKS Web based social networks (Boyd & Ellison, 2007; Golbeck, 2005) can be defined as follows: Services that are accessible over the web and allow individuals to (1) construct a public or semipublic profile within a system designed specifically to support social network connections, (2) articulate a list of other users with whom they share a connection, and (3) browse their list of connections and those made by others within the system. The study of web based social networks (WBSN) primarily focuses on connections between people. Scholars have examined the behavior of people in social network, how connections are formed and their evolution. WBSNs are a source of rich behavioral data. Data in WBSNs is extracted from user profiles and explicitly made connections between users. In WBSNs, much of the products, services, and features are based around social connections. Social connections are based on trust. Trust-aware recommender systems exploit these social connections or trust data as well profile data of users to create intelligent applications that provide personalized recommendations to users in WBSNs that are relevant and trustworthy. Trust is a social phenomenon. As a social concept, the many facets and influences of social trust has been extensively addressed in sociology and social psychology literature. It is a complex notion as a result it has many subtly different definitions. In Luhmann (1979), trust has been described as a tool for complexity reduction. Mayer, Davis and Schoorman (1995), proposed a trust model that is designed to focus on trust in an organizational setting. …