  Many real world person-person or person-product relationships can be modeled
graphically. More specifically, bipartite graphs can be especially useful when
modeling scenarios that involve two disjoint groups. As a result, many existing
papers have utilized bipartite graphs for the classical link recommendation
problem. In this paper, using the principle of bipartite graphs, we present
another approach to this problem with a two step algorithm that takes into
account frequency and similarity between common edges to make recommendations.
We test this approach with bipartite data gathered from the Epinions and
Movielens data sources, and find it to perform with roughly 14 percent error,
which improves upon baseline results. This is a promising result, and can be
refined to generate even more accurate recommendations.
