Hybrid recommendation methods in complex networks
This work addresses the need for more effective and adaptable recommendation systems in domains like e-commerce or social networks, but it is incremental as it builds on existing similarity measures.
The authors tackled the problem of improving recommendation systems in complex networks by proposing two new hybrid methods based on normalized similarity measures and convex combination of scores, achieving up to 20% performance improvement over existing non-parametric methods and showing robustness to noise in one algorithm.
We propose here two new recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three relevant data sets, and we compare their performance with several recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow to attain an improvement of performances of up to 20\% with respect to existing non-parametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we studied how an increasing presence of random links in the network affects the recommendation scores, and we found that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.