Abstract

Recommender systems attempt to find relevant data for their users. As the body of data available in the Web sphere becomes larger, this task becomes increasingly harder. In this paper we present a comparison of recommendation results when using different social and pseudo social features commonly available in online movie recommendation communities. Social relations, whether inferred or not, hold implicit information about users' taste and interests. We present results of a simple method that extends standard collaborative filtering algorithms to include a social network and show that this explicit and implicit information can be used to improve the quality of recommendations.

@inproceedings{said2011b,
author = {Alan Said and Ernesto William De Luca and Sahin Albayrak},
title = {Using Social and Pseudo Social Networks to Improve Recommendation},
year = {2011},
location = {Barcelona, Spain},
booktitle = {Proceedings of the 9th Workshop on Intelligent Techniques for Web Personalization (ITWP2011)},
}