  Recommender systems are important for e-commerce companies as well as
researchers. Recently, granular association rules have been proposed for
cold-start recommendation. However, existing approaches reserve only globally
strong rules; therefore some users may receive no recommendation at all. In
this paper, we propose to mine the top-k granular association rules for each
user. First we define three measures of granular association rules. These are
the source coverage which measures the user granule size, the target coverage
which measures the item granule size, and the confidence which measures the
strength of the association. With the confidence measure, rules can be ranked
according to their strength. Then we propose algorithms for training the
recommender and suggesting items to each user. Experimental are undertaken on a
publicly available data set MovieLens. Results indicate that the appropriate
setting of granule can avoid over-fitting and at the same time, help obtaining
high recommending accuracy.
