基于流行度的非平衡物质扩散推荐算法

doi:10.11772/j.issn.1001-9081.2015.12.3502

计算机应用 ›› 2015, Vol. 35 ›› Issue (12): 3502-3505.DOI: 10.11772/j.issn.1001-9081.2015.12.3502

基于流行度的非平衡物质扩散推荐算法

郭强¹, 宋文君¹, 胡兆龙², 侯磊¹, 张一璐¹, 陈芳娇¹

1. 上海理工大学管理学院, 上海 200093;
2. 北京师范大学系统科学学院, 北京 100875

收稿日期:2015-05-19 修回日期:2015-07-24 出版日期:2015-12-10 发布日期:2015-12-10
通讯作者: 郭强(1975-),女,辽宁大连人,教授,博士,主要研究方向:个性化推荐
作者简介:宋文君(1989-),女,辽宁大连人,硕士研究生,主要研究方向:个性化推荐、在线社会网络;胡兆龙(1987-),男,江西南昌人,博士研究生,主要研究方向:网络控制、传播动力学;侯磊(1990-),男,山东枣庄人,硕士研究生,主要研究方向:个性化推荐、社会动力学;张一璐(1989-),女,上海人,硕士研究生,主要研究方向:在线社会网络分析;陈芳娇(1992-),女,海南海口人,主要研究方向:个性化推荐。
基金资助:
国家自然科学基金资助项目(71371125,61374177,71171136);上海市一流学科(系统科学)资助项目(S1201YLXK);上海理工大学大学生创新创业计划项目(XJ2014104)。

Non-equilibrium mass diffusion recommendation algorithm based on popularity

GUO Qiang¹, SONG Wenjun¹, HU Zhaolong², HOU Lei¹, ZHANG Yilu¹, CHEN Fangjiao¹

1. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. School of System Science, Beijing Normal University, Beijing 100875, China

Received:2015-05-19 Revised:2015-07-24 Online:2015-12-10 Published:2015-12-10

摘要/Abstract

摘要： 针对产品的异质性没有在推荐算法中得到很好利用的问题,提出一种考虑产品流行度对用户兴趣偏好影响的物质扩散算法。通过模拟物质在用户-产品二部分网络上的扩散过程,并且引入产品流行度的可调参数,对产品流行度的影响进行定量刻画。在三个真实数据集上进行数值实验结果表明,该算法与经典的物质扩散算法相比,MovieLens、Netflix和Last.FM数据集上的平均排序打分可以分别提高25.60%、10.96%和1.2%;推荐列表多样性分别提高59.30%、53.07%和8.59%。所提出的非平衡的物质扩散算法所得到的结果更切合实际。

关键词: 个性化推荐, 非平衡物质扩散算法, 产品流行度, 二部分网络

Abstract: In order to solve the problem of not using the product heterogeneity well in recommendation algorithm, a modified mass diffusion algorithm was presented by considering the effect of the object popularity information on the user preference prediction. By introducing a tunable parameter of product popularity and simulating the mass diffusion process on the user-product bipartite network, the effect of the product popularity was quantitatively characterized. The experimental results on three empirical data sets which named MovieLens, Netflix and Last.FM show that, compared with the traditional mass diffusion method, the proposed algorithm can enhance the average ranking score by 25.6%, 10.96% and 1.2% respectively, and increase the diversity of the recommendation lists by 59.30%, 53.07% and 8.59% respectively. The proposed non-equilibrium mass diffusion algorithm can get more practical results.

Key words: personalized recommendation, Non-equilibrium Mass Diffusion (NMD) algorithm, product popularity, bipartite network

中图分类号:

TP301.6

郭强, 宋文君, 胡兆龙, 侯磊, 张一璐, 陈芳娇. 基于流行度的非平衡物质扩散推荐算法[J]. 计算机应用, 2015, 35(12): 3502-3505.

GUO Qiang, SONG Wenjun, HU Zhaolong, HOU Lei, ZHANG Yilu, CHEN Fangjiao. Non-equilibrium mass diffusion recommendation algorithm based on popularity[J]. Journal of Computer Applications, 2015, 35(12): 3502-3505.

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基于流行度的非平衡物质扩散推荐算法

Non-equilibrium mass diffusion recommendation algorithm based on popularity

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