A trust-aware collaborative filtering system based on weighted items for social tagging systems

Abstract

Collaborative Filtering systems consider users' social environment to predict what each user may like to visit in a social network i.e. they collect and analyze a large amount of information on users' behavior, activities or preferences and then predict or make suggestions to users. These systems use ranks or tags each user assign to different resources to make predictions. Lately, social tagging systems, in which users can insert new contents, tag, organize, share and search for contents, are becoming more popular. These social tagging systems have a lot of valuable information, but the data expansion in them is very fast and this has led to the need for recommender systems that will predict what each user may like or need make these suggestions to them. One of the problems in these systems is: “how much can we rely on the similar users, are they trustworthy?”. In this article we use trust metric, which we conclude from users' tagging behavior, beside similarities to give suggestions. Results show considering trust in a collaborative system can lead to better performance in generating suggestions.