  Granular association rules reveal patterns hide in many-to-many relationships
which are common in relational databases. In recommender systems, these rules
are appropriate for cold start recommendation, where a customer or a product
has just entered the system. An example of such rules might be "40% men like at
least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol."
Mining such rules is a challenging problem due to pattern explosion. In this
paper, we propose a new type of parametric rough sets on two universes to study
this problem. The model is deliberately defined such that the parameter
corresponds to one threshold of rules. With the lower approximation operator in
the new parametric rough sets, a backward algorithm is designed for the rule
mining problem. Experiments on two real world data sets show that the new
algorithm is significantly faster than the existing sandwich algorithm. This
study indicates a new application area, namely recommender systems, of
relational data mining, granular computing and rough sets.
