Exploiting positive and negative graded relevance assessments for content recommendation

Abstract

Social media allow users to give their opinion about the available content by assigning a rating. Collaborative filtering approaches to predict recommendations based on these graded relevance assessments are hampered by the sparseness of the data. This sparseness problem can be overcome with graph-based models, but current methods are not able to deal with negative relevance assessments. We propose a new graph-based model that exploits both positive and negative preference data. Hereto, we combine in a single content ranking the results from two graphs, one based on positive and the other based on negative preference information. The resulting ranking contains less false positives than a ranking based on positive information alone. Low ratings however appear to have a predictive value for relevant content. Discounting the negative information therefore does not only remove the irrelevant content from the top of the ranking, but also reduces the recall of relevant documents. © 2009 Springer Berlin Heidelberg.