Probabilistic Non-negative Inconsistent-resolution Matrices Factorization
Masahiro Kohjima, Tatsushi Matsubayashi, Hiroshi Sawada · 2015
In this paper, we tackle with the problem of analyzing datasets with different resolution such as a pair of user's individual data and user group's data, for example "userA visited shopA 5 times" and "users whose attributes are men purchased itemA 80 times in total". In order to establish a basic approach to this problem, we focus on the simplified scenario and propose a new probabilistic model called probabilistic non-negative inconsistent-resolution matrices factorization (pNimf). pNimf is rigorously derived from the data generative process using latent high-resolution data which underlie low-resolution data. We conduct experiments on real purchase log data and confirm that the proposed model provides superior performance, and that the performance improves as the number of low-resolution data increases. These results imply that our way of modeling using latent high-resolution data can become the basic approach to the problem of analyzing dataset with different resolution.