POI recommendation by incorporating trust-distrust relationship in LBSN

Jinghua ZHU; Qian MING

doi:10.11959/j.issn.1000-436x.2018117

您当前的位置：

首页 >

文章列表页 >

POI recommendation by incorporating trust-distrust relationship in LBSN

Papers | 更新时间：2024-06-05

- POI recommendation by incorporating trust-distrust relationship in LBSN
- Journal on Communications Vol. 39, Issue 7, Pages: 157-165(2018)
- 作者机构：
  
  黑龙江大学计算机科学与技术学院，黑龙江哈尔滨 150080
- 作者简介：
- 基金信息：
  
  The National Science Foundation for Young Scholars of China(61100048);The National Natural Science Foundation of China(61370222);The National Natural Science Foundation of China(F2016034)
- DOI：10.11959/j.issn.1000-436x.2018117
  CLC： TP311
- Online First：2018-07，
  
  Published：25 July 2018
- 稿件说明：
移动端阅览
Jinghua ZHU, Qian MING. POI recommendation by incorporating trust-distrust relationship in LBSN[J]. Journal on Communications, 2018, 39(7): 157-165.
DOI：

Jinghua ZHU, Qian MING. POI recommendation by incorporating trust-distrust relationship in LBSN[J]. Journal on Communications, 2018, 39(7): 157-165. DOI： 10.11959/j.issn.1000-436x.2018117.

摘要

兴趣点（POI

point of interest）推荐是位置社交网络（LBSN

location-based social network）重要的个性化服务，广泛用于热门景点推荐和旅游线路规划等。传统的基于协同过滤的推荐算法根据用户相似性和位置相似性进行推荐，未考虑推荐用户与目标用户间的信任关系，而信任关系有助于提高推荐系统的准确性、顽健性和用户满意度。首先分析了信任与不信任关系的传播特征，然后给出了信任度的表示和计算方法，最后提出了融合用户相似性、地理位置相似性和信任关系的混合推荐模型。实验结果表明，与传统协同过滤推荐方法相比，融合信任关系的混合推荐方法显著提高了推荐结果的准确性和用户满意度。

Abstract

POI (point of interest) recommendation is an important personalized service in the LBSN (location-based social network) which has wide applications such as popular sights recommendation and travel routes planning.Most existing collaborative filter algorithms make recommendation according to user similarity and location similarity

they don’t consider the trust relationship between users.And trust relationship is helpful to improve recommendation accuracy

robustness and user satisfaction.Firstly

the propagation property of trust and distrust relationship was analyzed.Then

the measurement and computation method of trust were given.Finally

a hybrid recommendation system which combined user similarity

geographical location similarity and trust relationship was proposed.The experiments results show that the hybrid recommendation is obviously superior to the traditional collaborative filtering in terms of results accuracy and user satisfaction.

关键词

Keywords

references

ZHENG Y . Location-based social networks:users [M ] . Springer New York , 2011 : 243 - 276 .

张富国 . 基于社交网络的个性化推荐技术 [J ] . 小型微型计算机系统 , 2014 , 35 ( 7 ): 1470 - 1476 .

ZHANG F G . Survey of online social network based personalized recommendation [J ] . Journal of Chinese Computer Systems , 2014 , 35 ( 7 ): 1470 - 1476 .

BHUIYAN T . A survey on the relationship between trust and interest similarity in online social networks [J ] . Journal of Emerging Technologies in Web Intelligence , 2010 , 2 ( 4 ): 291 - 299 .

CHEN L J , GAO J . A trust-based recommendation method using network diffusion processes [J ] ..2018:1-14. arXiv:1803.08378 . 2018 : 1 - 14 .

GUO G , . Integrating trust and similarity to ameliorate the data sparsity and cold start for recommender systems [C ] // ACM Conference on Recommender Systems . 2013 : 451 - 454 .

GUO G , ZHANG J , THALMANN D . Merging trust in collaborative filtering to alleviate data sparsity and cold start [J ] . Knowledge-Based Systems , 2014 , 57 ( 2 ): 57 - 68 .

YANG C , BAI L , ZHANG C , et al . Bridging collaborative filtering and semi-supervised learning:a neural approach for POI recommendation [C ] // ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 2017 : 1245 - 1254 .

ZHANG D C , LI M , WANG C D . Point of interest recommendation with social and geographical influence [C ] // IEEE International Conference on Big Data . 2017 : 1070 - 1075 .

LEE W P , MA C Y . Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks [M ] . Elsevier Science Publishers BV , 2016 .

YE M , YIN P , LEE W C , et al . Exploiting geographical influence for collaborative point-of-interest recommendation [C ] // International ACM Sigir Conference on Research ＆ Development in Information Retrieval . 2011 : 325 - 334 .

LIU B , FU Y , YAO Z , et al . Learning geographical preferences for point-of-interest recommendation [C ] // ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 2013 : 1043 - 1051 .

VICTOR P , VERBIEST N , CORNELIS C , et al . Enhancing the trust-based recommendation process with explicit distrust [J ] . ACM Transactions on the Web , 2013 , 7 ( 2 ):6.

MA X , LU H , GAN Z . Implicit trust and distrust prediction for recommender systems [J ] .2015:185-199. 2015 : 185 - 199 .

ZHOU T , LU L , ZHANG Y C . Predicting missing links via local information [J ] . European Physical Journal B , 2009 , 71 ( 4 ): 623 - 630 .

SYMEONIDIS P , TIAKAS E , MANOLOPOULOS Y . Transitive node similarity for link prediction in social networks with positive and negative links [C ] // ACM Conference on Recommender Systems . 2010 : 183 - 190 .

TOBLER W R . A computer movie simulating urban growth in the detroit region [J ] . Economic Geography , 1970 , 46 ( s1 ): 234 - 240 .

MASSA P , AVESANI P . Trust metrics on controversial users [J ] . International Journal on Semantic Web ＆ Information Systems , 2009 , 3 ( 1 ): 39 - 64 .

CARTWRIGHT D , HARARY F . Structural balance:a generalization of heider’ s theory [J ] . Psychological Review , 1956 , 63 ( 5 ):277.

Views

2012

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Collaborative filtering recommendation algorithm based on rough set rule extraction

Co-pairwise ranking model for item recommendation

Trust expansion and listwise learning-to-rank based service recommendation method

User fuzzy similarity-based collaborative filtering recommendation algorithm

User similarity-based collaborative filtering recommendation algorithm

Related Author

Zhipeng ZHANG

Yunpeng ZHANG

Yonggong REN

Bin WU

Yun CHEN

Zhongchuan SUN

Yangdong YE

Chen FANG

Related Institution

School of computer and information technology,Liaoning Normal University

School of Information Engineering, Zhengzhou University

The Third College,Information Engineering University

State Key Laboratory of Mathematical Engineering and Advanced Computing

National Digital Switching System Engineering and Technological R＆D Center

AI问答

⁰