  In this paper B-Rank, an efficient ranking algorithm for recommender systems,
is proposed. B-Rank is based on a random walk model on hypergraphs. Depending
on the setup, B-Rank outperforms other state of the art algorithms in terms of
precision, recall (19% - 50%), and inter list diversity (20% - 60%). B-Rank
captures well the difference between popular and niche objects. The proposed
algorithm produces very promising results for sparse and dense voting matrices.
Furthermore, a recommendation list update algorithm is introduced,to cope with
new votes. This technique significantly reduces computational complexity. The
implementation of the algorithm is simple, since B-Rank needs no parameter
tuning.
