Entities on social systems, such as users on Twitter, and images on Flickr, are at the core of many interesting applications: they can be ranked in search results, recommended to ...
People often find useful content on the web via social media. However, it is difficult to manually aggregate the information and recommendations embedded in a torrent of social ...
In this paper we propose a novel recommender system which enhances user-based collaborative filtering by using a trust-based social network. Our main idea is to use infinitesimal ...