A0509
Title: Augmented degree correction for bipartite networks with applications to recommender systems
Authors: Benjamin Leinwand - Stevens Institute of Technology (United States) [presenting]
Vladas Pipiras - University of North Carolina - Chapel Hill (United States)
Abstract: In recommender systems, users rate items and are subsequently served other product recommendations based on these ratings. Even though users usually rate a tiny percentage of the available items, the system tries to estimate unobserved preferences by finding similarities across users and across items. The observed rating data is treated as partially observed, dense, weighted, bipartite networks. For a class of systems without outside information, an approach developed for dense, weighted networks is adapted to account for unobserved edges and the bipartite nature of the problem. The approach begins with clustering both users and items into communities and locally estimates the patterns of ratings within each subnetwork induced by restricting attention to one community of users and one community of items community. The local fitting procedure relies on estimating local sociability parameters for every user and item and selecting the function to determine the degree correction contours that best model the underlying data. On a joke ratings data set, the proposed model performs better than existing alternatives in relatively sparse settings, though other approaches achieve better results when more data is available. The results indicate that despite struggling to pick up subtler signals, the proposed approach to recovery of large-scale, coarse patterns may still be useful in practical settings where high sparsity is typical.
