[1]张超杰,吴果林.基于约束推荐的网络可视化分析[J].计算机技术与发展,2019,29(10):115-119.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 024]
　ZHANG Chao-jie,WU Guo-lin.Network Visualization Analysis Based on Constraint Recommendation[J].,2019,29(10):115-119.[doi:10. 3969 / j. issn. 1673-629X. 2019. 10. 024]
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基于约束推荐的网络可视化分析()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年10期
页码:
115-119
栏目:
应用开发研究
出版日期:
2019-10-10

文章信息/Info

Title:
Network Visualization Analysis Based on Constraint Recommendation
文章编号:
1673-629X(2019)10-0115-05
作者:
张超杰1 ; 吴果林1; 2
1. 桂林航天工业学院 理学院,广西 桂林 541004; 2. 桂林航天工业学院 广西航空物流研究中心,广西 桂林 541004
Author(s):
ZHANG Chao-jie 1 ; WU Guo-lin 1; 2
1. School of Science,Guilin University of Aerospace Technology,Guilin 541004,China; 2. Guangxi Aviation Logistics Research Center,Guilin University of Aerospace Technology,Guilin 541004,China
关键词:
基于约束的推荐; 复杂网络; 二部图; 可视化
Keywords:
constraint-based recommendation; complex network; bipartite network; visualization
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 10. 024
摘要:
现如今互联网购物成为一种主要的购物方式,而各个购物平台都面临商品数据过量的问题,如何尽快地给出用户需求的商品列表就成为亟待解决的问题。 基于约束的推荐系统不依赖用户评分等用户偏好的历史数据,有效解决了“冷启动”的问题。 用户需求与用户意向物品的二部图网络包含了许多对推荐有价值隐藏在内部的信息,针对某在线销售记录的案例数据集进行研究,文中构建了二部图网络并对其进行了可视化,采用无标度网络模型(BA 模型)分析用户、物品节点的度。 由物品节点的度分布结构得出物品节点具有明显的社团属性,因此可以根据社团内部物品节点的度进行排序进行相应的推荐。 把这种推荐方法加入到推荐算法中,以增加推荐的精确度,为购物平台的推荐算法设计提供帮助。
Abstract:
At present,shopping on the Internet has become a major way of shopping,and each shopping platform is faced with the problem of excessive product data. How to give a list of products required by users as soon as possible has become an urgent problem to be solved. The constraint-based recommendation system does not rely on historical data such as user ratings and other user preferences, effectively solving the problem of “cold start”. The bipartite network of user needs and user-intentional items contains a lot of information that is valuable to the recommendation. We study the case data set of an online sales record,construct and visualize the bipartite graph network,and adopt a scale-free network model (BA model) to analyze the degree of user and item nodes. Since the degree distribution structure of the item node results in the item node having obvious community attribute, the corresponding recommendation can be performed according to the degree of the item node in the community. This recommendation method is added to the recommendation algorithm to increase the accuracy of the recommendation,which is helpful for the recommended algorithm design of the shopping platform.

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更新日期/Last Update: 2019-10-10