Abstract
We introduce variable precision rough set models to
a recommendation system using Pawlak's rough set theory we have proposed.
By extracting reducts and decision rules based on user's queries,
the proposed recommendation system recommends some goods that
the user may like though the characters are different with the queries.
However, too many goods may be recommended, and some of recommended goods have low connection with user's queries.
To avoid these problems, we propose a new recommendation method using variable precision rough set models,
and evaluate the proposed method by experiments.
