论文标题
加权双分链路推荐的两步方法
A Two Step Approach to Weighted Bipartite Link Recommendations
论文作者
论文摘要
许多现实世界的人物或人造关系可以以图形方式进行建模。更具体地说，在建模涉及两个不相交组的场景时，两分图尤其有用。结果，许多现有论文使用了两分图用于经典链接推荐问题。在本文中，使用两分图的原理，我们通过两步算法提出了另一种方法来解决此问题，该算法考虑了共同边缘之间的频率和相似性以提出建议。我们使用从上流和Movielens数据源收集的两分数据来测试这种方法，并发现它的性能大约为14％的误差，这在基线结果时会改善。这是一个有希望的结果，可以完善以产生更准确的建议。
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.
