Abstract

In a recommender system where users rate items we predict the rating of items users have not rated. We define a rating graph containing users and items as vertices and ratings as weighted edges. We extend the work of [1] that uses the resistance distance on the bipartite rating graph incorporating negative edge weights into the calculation of the resistance distance. This algorithm is then compared to other rating prediction algorithms using data from two rating corpora.

@INPROCEEDINGS{kunegis07a,
author = {Jerome Kunegis and Stephan Schmidt},
title = {Collaborative Filtering using Electrical Resistance Network Models
with Negative Edges},
booktitle = {Advances in Data Mining, Theoretical Aspects and Applications,
                  Proceedings of the 7th Industrial Conference, ICDM 2007},  
year = {2007},
owner = {scheel},
timestamp = {2007.05.02},
publisher = {Springer-Verlag},
pages= {269--282}
}