  These days, due to the increasing amount of information generated on the web,
most web service providers try to personalize their services. Users also
interact with web-based systems in multiple ways and state their interests and
preferences by rating the provided items. This paper proposes a framework to
predict users' demographic based on ratings registered by users in a system. To
the best of our knowledge, this is the first time that the item ratings are
employed for users' demographic prediction problems, which have extensively
been studied in recommendation systems and service personalization. We apply
the framework to the Movielens dataset's ratings and predict users' age and
gender. The experimental results show that using all ratings registered by
users improves the prediction accuracy by at least 16% compared with previously
studied models. Moreover, by classifying the items as popular and unpopular, we
eliminate ratings that belong to 95% of items and still reach an acceptable
level of accuracy. This significantly reduces update costs in a time-varying
environment. Besides this classification, we propose other methods to reduce
data volume while keeping the predictions accurate.
