本文针对推荐系统广泛存在的系统拥塞问题，首次提出了一种量化推荐拥塞程度的指标并比较了几种经典推荐算法的抗拥塞能力。结果显示推荐精度高的算法抗拥塞能力往往很差，而那些能较好防止推荐用塞的算法推荐精度又很低。为了解决这一两难问题，作者基于有向含权网络上的热传导过程提出一种新的推荐算法DWC(Directed Weighted Conduction)。在多个数据集上的实验表明该算法与以往经典算法相比，能够在保持推荐的准确性和多样性的同时，有效避免推荐系统陷入拥塞。本文提出的算法可应用于有限资源的产品或服务的推荐中，在电子商务领域具有广泛的应用前景。

论文下载地址：http://iopscience.iop.org/1367-2630/16/6/063057 

作者：Xiaolong Ren, Linyuan Lu*, Runran Liu and Jianlin Zhang

摘要：Recommender systems use the historical activities and personal profiles of users

to uncover their preferences and recommend objects. Most of the previous

methods are based on objects' (and/or users') similarity rather than on their

difference. Such approaches are subject to a high risk of increasingly exposing

users to a narrowing band of popular objects. As a result, a few objects may be

recommended to an enormous number of users, resulting in the problem of

recommendation congestion, which is to be avoided, especially when the

recommended objects are limited resources. In order to quantitatively measure a

recommendation algorithm's ability to avoid congestion, we proposed a new

metric inspired by the Gini index, which is used to measure the inequality of the

individual wealth distribution in an economy. Besides this, a new recommendation

method called directed weighted conduction (DWC) was developed by

considering the heat conduction process on a user-object bipartite network with

different thermal conductivities. Experimental results obtained for three benchmark

data sets showed that the DWC algorithm can effectively avoid system

congestion, and greatly improve the novelty and diversity, while retaining

relatively high accuracy, in comparison with the state-of-the-art methods.