Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 5/2019

23-02-2018 | Original Article

Friend recommendation in social networks based on multi-source information fusion

Authors: Shulin Cheng, Bofeng Zhang, Guobing Zou, Mingqing Huang, Zhu Zhang

Published in: International Journal of Machine Learning and Cybernetics | Issue 5/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Friend recommendation (FR) in social networks has been widely studied in recent years, which mainly focuses on social relationships and user interests. Friend of Friend method is one representative. However, the disadvantage is that most of existing solutions ignored other valuable information, such as user profile, location, influence and indirect trust. In fact, being friends among users is either determined by one or two dominant factors that originate from varying information sources, or the results of multiple main factors gaming. Motivated by the observations above, we propose a scalable FR framework in social networks, where multiple sources have been integrated based on improved D-S evidence theory. More specifically, we first analyzed 7 valuable information sources and categorized them into three classes, including Personal Features, Network Structure Features and Social Features. Furthermore, we also propose a fusion recommendation framework based on D-S evidence theory which embodies the minimal conflicts among evidences. In the proposed method, we first optimize the framework by importance degree and reliability of evidence based on original D-S evidence theory. Then, we designed a novel BPA evidence function by quantifying the evidence, where each evidence measures the relevance of forming friends among users. Finally, we describe the fusion FR algorithm plugged into our recommendation framework. The experiments on real-world dataset show that our proposed approach outperforms the other state-of-the-art algorithms on five evaluation metrics. The experimental results demonstrate the effectiveness of fusing multi-source information for FR in social networks.
previous article Per-sample prediction intervals for extreme learning machines
next article A new global robust stability condition for uncertain neural networks with discrete and distributed delays

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Show more products
Footnotes
5
http://​weibo.​com/​, a famous Chinese microblog web site which has far more than one hundred million users.
 
6
http://​t.​qq.​com/​, another well-known Chinese microblog web site analogue to Sina Weibo.
 
Literature
1.
Burke M, Marlow C, Lento T (2010) Social network activity and social well-being. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, New York, pp 1909–1912
2.
Trusov M, Bucklin RE, Pauwels K (2009) Effects of word-of-mouth versus traditional marketing: findings from an internet social networking site. J Mark 73(5):90–102CrossRef
3.
Mochón MC (2016) Social network analysis and big data tools applied to the systemic risk supervision. IJIMAI 3(6):34–37CrossRef
4.
Bakshy E, Rosenn I, Marlow C et al (2012) The role of social networks in information diffusion. In: Proceedings of the 21st international conference on World Wide Web. ACM, New York, pp 519–528
5.
He C, Li H, Fei X et al (2016) A topic community-based method for friend recommendation in large-scale online social networks. Practice and Experience, Concurrency and Computation
6.
Tang J, Hu X, Liu H (2013) Social recommendation: a review. Soc Netw Anal Min 3(4):1113–1133CrossRef
7.
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749CrossRef
8.
Linden G, Smith B, York J (2003) Amazon.com recommendations: Item-to-item collaborative filtering. Internet Comput IEEE 7(1):76–80CrossRef
9.
Sanjuan-Martinez O, G-Bustelo BCP, Crespo RG et al (2009) Using recommendation system for e-learning environments at degree level. Int J Interact Multimed Artif Intell 1(2):67–70
10.
Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Recommender systems handbook. Springer, New York, 1–35MATH
11.
Liu NN, He L, Zhao M (2013) Social temporal collaborative ranking for context aware movie recommendation. ACM Trans Intell Syst Technol (TIST) 4(1):15
12.
Yang X, Guo Y, Liu Y et al (2014) A survey of collaborative filtering based social recommender systems. Comput Commun 41:1–10CrossRef
13.
Li W, Ye Z, Xin M et al (2015) Social recommendation based on trust and influence in SNS environments. Multimed Tools Appl:1–18
14.
Gong NZ, Talwalkar A, Mackey L et al (2014) Joint link prediction and attribute inference using a social-attribute network. ACM Trans Intell Syst Technol (TIST) 5(2):27
15.
Yin Z, Gupta M, Weninger T et al (2010) A unified framework for link recommendation using random walks. Advances in Social Networks Analysis and Mining (ASONAM), 2010 International Conference on. IEEE, pp 152–159
16.
Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230CrossRef
17.
Agarwal V, Bharadwaj KK (2013) A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. Soc Netw Anal Min 3(3):359–379CrossRef
18.
Tang F, Zhang B, Zheng J et al (2013) Friend Recommendation Based on the Similarity of Micro-blog User Model. Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing. IEEE, pp 2200–2204
19.
Golbeck J, Hendler J (2006) Inferring binary trust relationships in web-based social networks. ACM Trans Internet Technol 6(4):497–529CrossRef
20.
Pazzani MJ (1999) A framework for collaborative, content-based and demographic filtering. Artif Intell Rev 13(5–6):393–408CrossRef
21.
Said A, Plumbaum T, De Luca EW et al (2011) A comparison of how demographic data affects recommendation. User Modeling, Adaptation and Personalization (UMAP)
22.
Kempe D, Kleinberg J, Tardos É (2003) Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp 137–146
23.
Nepal S, Paris C, Pour PA et al (2013) A social trust based friend recommender for online communities “invited paper”. In: Collaborative Computing: Networking, Applications and Worksharing (Collaboratecom), 2013 9th International Conference on. IEEE, pp 419–428
24.
Martinez-Cruz C, Porcel C, Bernabé-Moreno J et al (2015) A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Inf Sci 311:102–118CrossRef
25.
Guo D, Xu J, Zhang J et al (2017) User relationship strength modeling for friend recommendation on Instagram. Neurocomputing 239:9–18CrossRef
26.
Zhang Z, Liu Y, Ding W et al (2015) Proposing a new friend recommendation method, FRUTAI, to enhance social media providers’ performance. Decis Support Syst 79:46–54CrossRef
27.
Lü L, Zhou T (2011) Link prediction in complex networks: A survey. Phys A Stat Mech Appl 390(6):1150–1170CrossRef
28.
Getoor L, Diehl CP (2005) Link mining: a survey. ACM SIGKDD Explor Newsl 7(2):3–12CrossRef
29.
Clauset A, Moore C, Newman MEJ (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453(7191):98–101CrossRef
30.
Liu W, Lü L (2010) Link prediction based on local random walk. EPL 89(5):58007CrossRef
31.
Chen J, Geyer W, Dugan C et al (2009) Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, New York, pp 201–210
32.
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031CrossRef
33.
Yang SH, Long B, Smola A et al (2011) Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of the 20th international conference on World Wide Web. ACM, New York, pp 537–546
34.
Yu Z, Wang C, Bu J et al (2015) Friend recommendation with content spread enhancement in social networks. Inf Sci 309:102–118CrossRef
35.
Brin S, Page L (2012) Reprint of: The anatomy of a large-scale hypertextual web search engine. Comput Netw 56(18):3825–3833CrossRef
36.
Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, New York, pp 195–202
37.
38.
Cui L, Dong L, Fu X et al (2017) A video recommendation algorithm based on the combination of video content and social network. Concurr Comput Pract Exp 29(14)
39.
Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the fourth ACM conference on Recommender systems. ACM, pp 135–142
40.
Ma H, King I, Lyu MR (2009) Learning to recommend with social trust ensemble. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval. ACM, New York, pp 203–210
41.
Agarwal V, Bharadwaj KK (2011) Trust-enhanced recommendation of friends in web based social networks using genetic algorithms to learn user preferences. In: Trends in Computer Science, Engineering and Information Technology. Springer, Berlin, Heidelberg, pp 476–485
42.
Ma Y, Yu Z, Ding JA (2014) Method of user recommendation in social networks based on trust relationship and topic similarity. Social media processing. Springer, Berlin, pp 240–251
43.
Agarwal V, Bharadwaj KK (2014) Friends recommendations in dynamic social networks. Encycl Soc Netw Anal Min:553–562
44.
Victor P, Cornelis C, De Cock M et al (2011) Practical aggregation operators for gradual trust and distrust. Fuzzy Sets Syst 184(1):126–147MathSciNetMATHCrossRef
45.
Bao J, Zheng Y, Wilkie D et al (2013) A survey on recommendations in location-based social networks. ACM Transaction on Intelligent Systems and Technology, New York
46.
Wang Y, Wang X, Zuo W-L (2014) Trust prediction modeling based on social theories. J Softw (Chin) 25(12):2893–2904MathSciNet
47.
Milgram S (1967) The small world problem. Psychol Today 2(1):60–67
48.
Shafer G (1976) A mathematical theory of evidence. Princeton university press, PrincetonMATH
49.
Telmoudi A, Chakhar S (2004) Data fusion application from evidential databases as a support for decision making. Inf Softw Technol 46(8):547–555CrossRef
50.
51.
Khaleghi B, Khamis A, Karray FO et al (2013) Multisensor data fusion: a review of the state-of-the-art. Inf Fusion 14(1):28–44CrossRef
52.
Wei D, Deng X, Zhang X et al (2013) Identifying influential nodes in weighted networks based on evidence theory. Phys A Stat Mech Appl 392(10):2564–2575CrossRef
53.
Ellison NB (2007) Social network sites: Definition, history, and scholarship. J Comput Mediat Commun 13(1):210–230MathSciNetCrossRef
54.
Yong D, WenKang S, ZhenFu Z et al (2004) Combining belief functions based on distance of evidence. Decis Support Syst 38(3):489–493CrossRef
55.
Gong NZ, Xu W, Huang L et al (2012) Evolution of social-attribute networks: measurements, modeling, and implications using google+. In: Proceedings of the 2012 Internet Measurement Conference. ACM, pp 131–144
56.
Li Y, McLean D, Bandar Z et al (2006) Sentence similarity based on semantic nets and corpus statistics. Knowl Data Eng IEEE Trans 18(8):1138–1150CrossRef
57.
DeScioli P, Kurzban R, Koch EN et al (2011) Best friends alliances, friend ranking, and the myspace social network. Perspect Psychol Sci 6(1):6–8CrossRef
58.
Liben-Nowell D, Novak J, Kumar R et al (2005) Geographic routing in social networks. Proc Natl Acad Sci USA 102(33):11623–11628
59.
Sui X, Chen Z, Ma J (2015) Location sensitive friend recommendation in social network. Web technologies and applications. Springer International Publishing, New York, pp 316–327
60.
Scellato S, Noulas A, Lambiotte R et al (2011) Socio-spatial properties of online location-based social networks. ICWSM 11:329–336
61.
Wu M, Wang Z, Sun H et al (2016) Friend Recommendation Algorithm for Online Social Networks Based on Location Preference. Information Science and Control Engineering (ICISCE), 3rd International Conference on. IEEE, 2016: 379–385
62.
Li Q, Zheng Y, Xie X et al (2008) Mining user similarity based on location history. In: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems. ACM, New York, p 34
63.
Zheng Y, Zhou X (2011) Computing with spatial trajectories. Springer Science & Business Media, New YorkCrossRef
64.
Xiao X, Zheng Y, Luo Q et al (2014) Inferring social ties between users with human location history. J Ambient Intell Humaniz Comput 5(1):3–19CrossRef
65.
Moreno A, Redondo T (2016) Text analytics: the convergence of big data and artificial intelligence. IJIMAI 3(6):57–64CrossRef
66.
Gruber TR (1993) A translation approach to portable ontology specifications. Knowl Acquis 5(2):199–220CrossRef
67.
Natalya FN, Deborah LM (2001) Ontology development 101: a guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05 and Stanford Medical Informatics Technical Report SMI-2001-0880
68.
Zheng J, Zhang B, Yue X et al (2015) Neighborhood-user profiling based on perception relationship in the micro-blog scenario. Web Semant Sci Serv Agents World Wide Web 34:13–26CrossRef
69.
Cha M, Haddadi H, Benevenuto F et al (2010) Measuring User Influence in Twitter: The Million Follower Fallacy. ICWSM 10(10–17):30
70.
Golbeck J (2005) Computing and applying trust in web-based social networks. University of Maryland, Maryland
71.
Victor P, Verbiest N, Cornelis C et al (2013) Enhancing the trust-based recommendation process with explicit distrust. ACM Trans Actions Web (TWEB) 7(2):6
72.
Luo H, Niu C, Shen R et al (2008) A collaborative filtering framework based on both local user similarity and global user similarity. Mach Learn 72(3):231–245CrossRef
73.
Avnit A (2009) The million followers fallacy. Pravda Media Group, Tel Aviv
74.
Verbiest N, Cornelis C, Victor P et al (2012) Trust and distrust aggregation enhanced with path length incorporation. Fuzzy Sets Syst 202:61–74MathSciNetCrossRef
75.
Shani G, Gunawardana A (2011) Evaluating recommendation systems. Recommender systems handbook. Springer, New York, pp 257–297CrossRef
76.
Weimer M, Karatzoglou A, Le QV et al (2008) Cofi rank-maximum margin matrix factorization for collaborative ranking. In: Advances in neural information processing systems, pp 1593–1600
77.
Chakrabarti S, Khanna R, Sawant U et al (2008) Structured learning for non-smooth ranking losses. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 88–96
78.
Järvelin K, Kekäläinen J (2000) IR evaluation methods for retrieving highly relevant documents. In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 41–48
Metadata
Title
Friend recommendation in social networks based on multi-source information fusion
Authors
Shulin Cheng
Bofeng Zhang
Guobing Zou
Mingqing Huang
Zhu Zhang
Publication date
23-02-2018
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 5/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0778-1

Other articles of this Issue 5/2019

International Journal of Machine Learning and Cybernetics 5/2019 Go to the issue

Original Article

A fast iterative algorithm for support vector data description

Original Article

Global stability analysis of delayed complex-valued fractional-order coupled neural networks with nodes of different dimensions

Original Article

Adaptive fuzzy-neural-network based on RBFNN control for active power filter

Original Article

Stability investigation of multi-objective heuristic ensemble classifiers

Original Article

Efficient collaborative filtering recommendations with multi-channel feature vectors

Original Article

Normal neutrosophic frank aggregation operators and their application in multi-attribute group decision making