[1]雷震,文益民,王志强,等.基于影响力控制的热传导算法[J].智能系统学报编辑部,2016,11(3):328-335.[doi:10.11992/tis.201603042]
LEI Zhen,WEN Yimin,WANG Zhiqiang,et al.Heat conduction controlled by the influence of users and items[J].CAAI Transactions on Intelligent Systems,2016,11(3):328-335.[doi:10.11992/tis.201603042]
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LEI Zhen,WEN Yimin,WANG Zhiqiang,et al.Heat conduction controlled by the influence of users and items[J].CAAI Transactions on Intelligent Systems,2016,11(3):328-335.[doi:10.11992/tis.201603042]
基于影响力控制的热传导算法
《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第3期
页码:
328-335
栏目:
学术论文—自然语言处理与理解
出版日期:
2016-06-25
- Title:
- Heat conduction controlled by the influence of users and items
- Keywords:
- heat conduction; personalized recommendation; user’s preference; sentiment polarity; bipartite network; information overload; item popularity; user’s influence
- 分类号:
- TP391
- 摘要:
- 因特网上信息严重过载,使得用户不容易从纷繁的信息中找到适合自己的内容。如何准确地向用户推荐他们想要的信息成为急待解决的问题。热传导算法(HC)被广泛地应用于个性化推荐领域,但是它的热量传播机制不利于经历丰富的用户喜欢的流行物品得到更多的热量。因此,本文提出了基于影响力控制的热传导算法(THC)。THC引入两个参数控制度数大的用户喜欢的度数大的物品对目标用户推荐的影响。另外,本文提出利用用户对景点的各项评分及评论的情感极性来判断用户是否喜欢一个景点,还提出了一个新的指标buir以度量度数大的用户喜欢的度数大的物品出现在推荐列表中的比例。实验结果表明:适度增大的度数大的用户喜欢的度数大的物品的影响,有助于推荐出目标用户喜欢的物品,从而有助于提升推荐效果。
- Abstract:
- The overload of information on the Internet can lead to users feeling hopeless about finding the information they are seeking. Making accurate recommendations to users about the information they truly need is an urgent problem that must be addressed. The heat conduction (HC) algorithm has recently been applied in personalized recommendation technology, but its mechanism weakens the heat generated from the larger-degree itemsliked by the larger-degree users. To solve this problem, we propose an improved HC algorithm that is based on user influence control (THC). THC introduces two tunable parameters to better control the influence of larger-degree users’ preferences for larger-degree items on target users. We also consider a user’s comment scores and the sentiment polarity of a comment in a given scenario to accurately judge whether the user truly likes the given scenario. We also propose a new index, called a buir, which measures the ratio of the larger-degree items that are liked by larger-degree users on the recommendation list. Experimental results show that appropriately promoting the influence of larger-degree items that are liked by larger-degree users helps in making recommendations to target users regarding items in which they are truly interested, thereby improving the performance of the recommendation.
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备注/Memo
收稿日期:2016-3-19;改回日期:。
基金项目:国家自然科学基金项目(61363029);广西省科学研究与技术开发项目(桂科攻14124005-2-1);湖南省博士后科研专项计划项目(2011RS4073);广西信息科学中心项目(YB408).
作者简介:雷震,男,1991年生,硕士研究生,主要研究方向为推荐系统与数据挖掘。文益民,男,1969年生,博士,教授,中国计算机学会高级会员。主要研究方向为机器学习与数据挖掘、极化SAR图像处理、社会计算。主持省部级科研项目8项,获得省部级教学、科研奖励5项,发表学术论文30余篇,其中被SCI、EI收录18篇,翻译译著1部。王志强,男,1991年生,硕士研究生,主要研究方向为数据挖掘、旅游推荐。
通讯作者:文益民.E-mail:ymwen2004@aliyun.com.
更新日期/Last Update:
1900-01-01