NJP -- Avoiding congestion in recommender systems 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 algorithmʼs 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.
Xiaolong Ren, Linyuan Lü*, Runran Liu and Jianlin Zhang -
Ren, X., Lü, L., Liu, R., & Zhang, J. (2014). Avoiding congestion in recommender systems. New Journal of Physics, 16(6), 063057. -
科学通报 -- 网络重要节点排序方法综述(Review of ranking nodes in complex networks) 复杂网络的重要节点是指相比网络其他节点而言，能够在更大程度上影响网络的结构与功能的一些特殊节点. 近年来，节点重要性排序研究受到越来越广泛的关注，不仅因为其重大的理论研究意义，更因为其广泛的实际应用价值. 由于应用领域极广，且不同类型的网络中节点的重要性评价方法各有侧重，学者们从不同的实际问题出发设计出各种各样的方法. 本文系统地综述了复杂网络领域具有代表性的30余种重要节点挖掘方法，并将其分为四大类，详细比较各种方法的计算思路、应用场景和优缺点. 在此基础上，本文分析了重要节点排序研究现存的一些问题，并展望了若干重要的开放性问题.
The important nodes in complex networks are the extraordinary nodes which play more significant role than other nodes on the structure and function of the networks. In recent years, the reaserch on indentifying inflential nodes in complex networks has attracted much attention, because of its great theoretical significance as well as the wide range of applications. Aiming at different types of networks and motivated by different problems and applications, researchers have proposed groups of methods. This article systematically reviews more than 30 representative methods which are classified into four categories, and detailedly compares them from the aspects of computing ideas and application scenarios, and futher analyzes the strongness and weakness of each method. On this basis, this article summarizes the existing problems and outlines eight open issues as main challenges in the near future.
Xiaolong Ren, Linyuan Lü* -
REN XiaoLong, LÜ LinYuan. Review of ranking nodes in complex networks. Chinese Science Bulletin, 2014, 59(13): 1175-1197. -