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.
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Kunegis, J., Schmidt, S. (2007). Collaborative Filtering Using Electrical Resistance Network Models. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_21
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DOI: https://doi.org/10.1007/978-3-540-73435-2_21
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