Trust-Based Infinitesimals for Enhanced Collaborative Filtering

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 numbers and polynomials for capturing natural preferences in aggregating opinions of trusted users. We use these opinions to "help" users who are similar to an active user to come up with recommendations for items for which they might not have an opinion themselves. We argue that the method we propose reflects better the real life behaviour of the people. Our method is justified by the experimental results; we are the first to break a stated "barrier" of 0.73 for the mean absolute error (MAE) of the predicted ratings. Our results are based on a large, real life dataset from Epinions.com, for which, we also achieve a prediction coverage that is significantly better than that of the state-of-the-art methods.

• Type: Conference
• Year: 2009
• Where: COMAD
• Authors: Maria Chowdhury, Alex Thomo, William W. Wadge
• Keywords: COMAD 2008, COMAD 2009, Real Life, Trust-based Social Network, User-based Collaborative Filtering