EXTRA: EXpertise-Boosted Model for Trust-Based Recommendation System Based on Supervised Random Walk

Abstract

The quality of recommendations based on any class of recommender systems may become poor if no or low quality data has been provided by users. This is a situation known as it Cold Start problem, which typically happens when a new user registers to the system and no preference data is available for that user. Trust-Aware Recommendation Systems can be considered as a solution for the cold start problem. In these systems, the trust between users plays an import role for making recommendations. However, most of the Trust-Aware RSs consider trust as a context independent phenomenon which means if user a trusts user b to the degree k then user a trusts user b to the degree k in all the concepts. However, in reality, trust is context dependent and user a can trust user b in context X but not in Y. Moreover, most of the trust-aware RSs do not consider an expertise concept for users and all the users are considered as same in the recommendation process. In this paper we proposed a novel approach for detecting expert users just based on their ratings (unlike previous systems which consider the separate profile and extra information for each user to find an expert). In this model a supervised random walk is exploited to search the trust network for finding experts. Empirical experiments on the Epinions dataset shows that EXTRA can outperform previous models in terms of accuracy and coverage.