Abstract
Neighborhood-based approaches often fail in sparse scenarios; a direct implication for recommender systems exploiting co-occurring items is often an inappropriately poor performance. As a remedy, we propose to propagate information (e.g., similarities) across the item graph to leverage sparse data. Instead of processing only directly connected items (e.g. co-occurrences), the similarity of two items is defined as the maximum capacity path interconnecting them. Our approach resembles a generalization of neighborhood-based methods that are obtained as special cases when restricting path lengths to one. We present two efficient online computation schemes and report on empirical results.
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Note that paths are usually cycle free by definition and capacities do not change by repeating cycles.
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Acknowledgments
This research has been funded in parts by the German Federal Ministry of Education and Science BMBF under grant QQM/01LSA1503C and the Brazilian CAPES Foundations and a grant from CNPq.
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Boubekki, A., Brefeld, U., Lucchesi, C.L., Stille, W. (2017). Propagating Maximum Capacities for Recommendation. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds) KI 2017: Advances in Artificial Intelligence. KI 2017. Lecture Notes in Computer Science(), vol 10505. Springer, Cham. https://doi.org/10.1007/978-3-319-67190-1_6
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