The impact of multifaceted trust component in estimating product rating for Recommender Systems

Given the overwhelming amount of information on the web, users face many options when selecting products or services. Recommender systems build a model based on information from user's past choices and ratings, related or trusted individuals, previously selected products, and the features of such products; the system then prioritize items to recommend them to the user based on this model. Trust aware method will use the trust network between users for estimating products ratings. Researchers have been interested in subject of trust in different facets because of different level of trust in professional fields. This article presents multi-faceted trust model for estimating product ratings, in which users and items are considered due to amount of dependency to each facet and also level of trust in it. Epinions dataset analysis indicates that distance dispersion of users’ choice in a multi-facted trust network is significantly lower than their distribution in a general trust network. Then baseline and similarity base models’ performance have been checked and compared in forms of general and multi-faceted. Model evaluation has been done based on Root Mean Squared Error and Epinions dataset separation in two groups of test and train and also croos validation method. Results indicate that estimation error has been averagely decreased 20% and improve recommender system performance obviously by considering trust component in multi-facted form.