A Two-Step Approach to Weighted Bipartite Link Recommendations

Abstract

Many real-world person-person or person-product relationships can be modeled graphically. Specifically, bipartite graphs are especially useful when modeling scenarios involving two disjoint groups. As a result, existing papers have utilized bipartite graphs to address the classical link recommendation problem. Applying the principle of bipartite graphs, this research presents a modified approach to this problem which employs a two-step algorithm for making recommendations that accounts for the frequency and similarity between common edges. Implemented in Python, the new approach was tested using bipartite data from Epinions and Movielens data sources. The findings showed that it improved the baseline results, performing within an estimated error of 14 per cent. This two-step algorithm produced promising findings, and can be refined to generate recommendations with even greater accuracy.