[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]
点击复制

基于影响力控制的热传导算法(/HTML)
分享到:

《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年3期
页码:
328-335
栏目:
出版日期:
2016-06-25

文章信息/Info

Title:
Heat conduction controlled by the influence of users and items
作者:
雷震1 文益民12 王志强1 缪裕青12
1. 桂林电子科技大学 计算机与信息安全学院, 广西 桂林 541004;
2. 桂林电子科技大学 广西可信软件重点实验室, 广西 桂林 541004
Author(s):
LEI Zhen1 WEN Yimin12 WANG Zhiqiang1 MIAO Yuqing12
1. School of Computer Science and Information Security, Guilin 541004, China;
2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
关键词:
热传导个性化推荐用户偏好情感极性二部网络信息过载物品流行度用户影响力
Keywords:
heat conductionpersonalized recommendationuser’s preferencesentiment polaritybipartite networkinformation overloaditem popularityuser’s influence
分类号:
TP391
DOI:
10.11992/tis.201603042
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.

参考文献/References:

[1] 文益民, 史一帆, 蔡国永, 等. 个性化旅游推荐研究综述[EB/OL]. 北京: 中国科技论文在线, 2014. [2014-07-03]. http://www.paper.edu.cn/releasepaper/content/201407-56.
[2] RESNICK P, VARIAN H R. Recommender systems[J]. Communications of the ACM, 1997, 40(3): 56-58.
[3] ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions[J]. IEEE transactions on knowledge and data engineering, 2005, 17(6): 734-749.
[4] FELFERNIG A, GORDEA S, JANNACH D, et al. A short survey of recommendation technologies in travel and tourism[J]. OEGAI journal, 2007, 25(7): 17-22.
[5] LIU Jianguo, ZHOU Tao, GUO Qiang. Information filtering via biased heat conduction[J]. Physical review E, 2011, 84(3): 037101.
[6] LINDEN G, SMITH B, YORK J. Amazon. com recommendations: item-to-item collaborative filtering[J]. IEEE internet computing, 2003, 7(1): 76-80.
[7] DAS A S, DATAR M, GARG A, et al. Google news personalization: scalable online collaborative filtering[C]//Proceedings of the 16th International Conference on World wide Web. New York, USA, 2007: 271-280.
[8] LIU Qiwen, CHEN Tianjian, CAI Jing, et al. Enlister: baidu’s recommender system for the biggest chinese Q & A website[C]//Proceedings of the Sixth ACM Conference on Recommender Systems. New York, USA, 2012: 285-288.
[9] HERLOCKER J L, KONSTAN J A, RIEDL J. Explaining collaborative filtering recommendations[C]//Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work. New York, USA, 2000: 241-250.
[10] PAZZANI M J. A framework for collaborative, content-based and demographic filtering[J]. Artificial intelligence review, 1999, 13(5-6): 393-408.
[11] ZHOU Tao, Lü Linyuan, ZHANG Yicheng. Predicting missing links via local information[J]. The european physical journal B, 2009, 71(4): 623-630.
[12] Lü Linyuan, ZHOU Tao. Link prediction in weighted networks: the role of weak ties[J]. EOL (europhysics letters), 2010, 89(1): 18001.
[13] ZHOU Tao, KUSCSIK Z, LIU Jianguo, et al. Solving the apparent diversity-accuracy dilemma of recommender systems[J]. Proceedings of the national academy of sciences of the United States of America, 2010, 107(10): 4511-4515.
[14] ZENG Wei, SHANG Mingsheng, ZHANG Qianming, et al. Can dissimilar users contribute to accuracy and diversity of personalized recommendation[J]. International journal of modern physics C, 2010, 21(10): 1217-1227.
[15] ZHANG Zike, YU Lu, FANG Kuan, et al. Website-oriented recommendation based on heat spreading and tag-aware collaborative filtering[J]. Physica A: statistical mechanics and its applications, 2014, 399: 82-88.
[16] ZHOU Tao, REN Jie, MEDO M, et al. Bipartite network projection and personal recommendation[J]. Physical review E, 2007, 76(4): 046115.
[17] NIE Dacheng, AN Yahui, DONG Qiang, et al. Information filtering via balanced diffusion on bipartite networks[J]. Physica A: statistical mechanics and its applications, 2015, 421: 44-53.
[18] 侯磊, 胡兆龙, 张博, 等. 基于流行度的非平衡热传导推荐算法研究[J]. 计算机应用研究, 2015, 32(11): 3235-3237. HOU Lei, HU Zhaolong, ZHANG Bo, et al. Information filtering via non-equilibrium heat conduction with consideration of popularity[J]. Application research of computers, 2015, 32(11): 3235-3237.
[19] LIU Jianguo, GUO Qiang, ZHANG Yicheng. Information filtering via weighted heat conduction algorithm[J]. Physica A: statistical mechanics and its applications, 2011, 390(12): 2414-2420.
[20] SHI Shaoliang, LI Yunpeng, WEN Yimin, et al. Adding the sentiment attribute of nodes to improve link prediction in social network[C]//Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery. Zhangjiajie, China, 2015: 1263-1269.
[20] LIU Jinhu, ZHANG Zike, CHEN Lingjiao, et al. Gravity effects on information filtering and network evolving[J]. PLoS one, 2014, 9(3): e91070.

相似文献/References:

[1]陈万志,林澍,王丽,等.基于用户移动轨迹的个性化健康建议推荐方法[J].智能系统学报编辑部,2016,11(2):264.[doi:10.11992/tis.201511026]
 CHEN Wanzhi,LIN Shu,WANG Li,et al.Personalized recommendation algorithm of health advice based on the user’s mobile trajectory[J].CAAI Transactions on Intelligent Systems,2016,11(3):264.[doi:10.11992/tis.201511026]
[2]常亮,孙文平,张伟涛,等.旅游路线规划研究综述[J].智能系统学报编辑部,2019,14(01):82.[doi:10.11992/tis.201804005]
 CHANG Liang,SUN Wenping,ZHANG Weitao,et al.Review of tourism route planning[J].CAAI Transactions on Intelligent Systems,2019,14(3):82.[doi:10.11992/tis.201804005]
[3]匡海丽,常亮,宾辰忠,等.上下文感知旅游推荐系统研究综述[J].智能系统学报编辑部,2019,14(04):611.[doi:10.11992/tis.201901013]
 KUANG Haili,CHANG Liang,BIN Chenzhong,et al.Review of a context-aware travel recommendation system[J].CAAI Transactions on Intelligent Systems,2019,14(3):611.[doi:10.11992/tis.201901013]
[4]YOCHUM Phatpicha,常亮,古天龙,等.基于位置和开放链接数据的旅游推荐系统综述[J].智能系统学报编辑部,2020,15(1):25.[doi:10.11992/tis.201912023]
 YOCHUM Phatpicha,CHANG Liang,GU Tianlong,et al.A review of linked open data in location-based recommendation system in the tourism domain[J].CAAI Transactions on Intelligent Systems,2020,15(3):25.[doi:10.11992/tis.201912023]

备注/Memo

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