  Cognitive inference of user demographics, such as gender and age, plays an
important role in creating user profiles for adjusting marketing strategies and
generating personalized recommendations because user demographic data is
usually not available due to data privacy concerns. At present, users can
readily express feedback regarding products or services that they have
purchased. During this process, user demographics are concealed, but the data
has never yet been successfully utilized to contribute to the cognitive
inference of user demographics. In this paper, we investigate the inference
power of user ratings data, and propose a simple yet general cognitive
inference model, called rating to profile (R2P), to infer user demographics
from user provided ratings. In particular, the proposed R2P model can achieve
the following: 1. Correctly integrate user ratings into model training. 2.Infer
multiple demographic attributes of users simultaneously, capturing the
underlying relevance between different demographic attributes. 3. Train its two
components, i.e. feature extractor and classifier, in an integrated manner
under a supervised learning paradigm, which effectively helps to discover
useful hidden patterns from highly sparse ratings data. We introduce how to
incorporate user ratings data into the research field of cognitive inference of
user demographic data, and detail the model development and optimization
process for the proposed R2P. Extensive experiments are conducted on two
real-world ratings datasets against various compared state-of-the-art methods,
and the results from multiple aspects demonstrate that our proposed R2P model
can significantly improve on the cognitive inference performance of user
demographic data.
