Social Trust Prediction via Max-norm Constrained 1-bit Matrix Completion
This work addresses social trust prediction for users in social networks, but it is incremental as it builds on existing matrix completion methods with specific adaptations.
The paper tackled the problem of predicting social trust in networks by formulating it as a 1-bit matrix completion with non-uniform sampling, and proposed a max-norm constrained method that demonstrated superiority on two benchmark datasets.
Social trust prediction addresses the significant problem of exploring interactions among users in social networks. Naturally, this problem can be formulated in the matrix completion framework, with each entry indicating the trustness or distrustness. However, there are two challenges for the social trust problem: 1) the observed data are with sign (1-bit) measurements; 2) they are typically sampled non-uniformly. Most of the previous matrix completion methods do not well handle the two issues. Motivated by the recent progress of max-norm, we propose to solve the problem with a 1-bit max-norm constrained formulation. Since max-norm is not easy to optimize, we utilize a reformulation of max-norm which facilitates an efficient projected gradient decent algorithm. We demonstrate the superiority of our formulation on two benchmark datasets.