A Two Step Approach to Weighted Bipartite Link Recommendations
This is an incremental improvement for recommendation systems using bipartite graphs.
The paper tackles the bipartite link recommendation problem by proposing a two-step algorithm that considers frequency and similarity between common edges. It achieves approximately 14% error on Epinions and Movielens datasets, improving upon baseline results.
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.