Skip to main content

Advertisement

SpringerLink
Log in

Circle-based Group Recommendation in Social Networks

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

A large amount of data available on Web has proven to be an immense resource for innovative recommender system (RS) techniques and concepts. The traditional recommender system intended to provide recommendations for a single user. However, in certain domains the recommendation is required for a group of users. As to provide better recommendations for a group of users, we leverage the concept of circles in a network. In this work, we use the genetic algorithm (GA) K_Means clustering algorithm to generate social circles in a network. Then, we compute the status of each user in these overlapping circles. Finally, a circle-based group recommendation approach is used to generate the final group recommendation. The results obtained on the Epinions dataset validate the eminence of the proposed model over traditional approaches of group recommendation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.trustlet.org/downloaded_epinions.html.

References

  • 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 6:734–749

    Article  Google Scholar 

  • Agarwal V, Bharadwaj KK (2015) Predicting the dynamics of social circles in ego networks using pattern analysis and GA K-means clustering. Wiley Interdiscip Rev Data Min Knowl Discov 5(3):113–141

    Article  Google Scholar 

  • Ardissono L, Goy A, Petrone G, Segnan M, Torasso P (2003) Intrigue: personalized recommendation of tourist attractions for desktop and hand held devices. Appl Artif Intell 17(8–9):687–714

    Article  Google Scholar 

  • Baltrunas L, Makcinskas T, Ricci F (2010) Group recommendations with rank aggregation and collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems, pp 119–126. ACM

  • Baskin JP, Krishnamurthi S (2009) Preference aggregation in group recommender systems for committee decision-making. In: Proceedings of the third ACM conference on Recommender systems, pp 337–340. ACM

  • Boratto L, Carta S (2010) State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Information retrieval and mining in distributed environments, pp 1–20. Berlin Heidelberg: Springer

  • Choudhary N, Bharadwaj KK (2018) Evolutionary learning approach to multi-agent negotiation for group recommender systems. Multimedia Tools Appl 1–23

  • Choudhary N, Bharadwaj KK (2019) Leveraging trust behaviour of users for group recommender systems in social networks. In: Integrated Intelligent Computing, Communication and Security, pp 41–47. Springer, Singapore

  • Christensen I, Schiaffino S, Armentano M (2016) Social group recommendation in the tourism domain. Journal of Intelligent Information Systems, pp 1–23

  • Crossen A, Budzik J, Hammond KJ (2002). Flytrap: intelligent group music recommendation. In: Proceedings of the 7th international conference on Intelligent user interfaces, pp 184–185. ACM

  • Dara S, Chowdary CR, Kumar C (2019) A survey on group recommender systems. J Intell Inf Syst 1–25

  • Eiben AE, Smith JE (2015) Introduction to evolutionary computing. Springer

  • Felfernig A, Boratto L, Stettinger M, Tkalčič M (2018) Evaluating group recommender systems. In: Group Recommender Systems, pp 59–71. Springer, Cham

  • Girdhar N, Bharadwaj KK (2019) Social status computation for nodes of overlapping communities in directed signed social networks. In: Integrated Intelligent Computing, Communication and Security, pp 49–57. Springer, Singapore

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company Inc, Boston

    MATH  Google Scholar 

  • Jameson A (2004) More than the sum of its members: challenges for group recommender systems. In: Proceedings of the working conference on Advanced visual interfaces, pp. 48–54. ACM

  • Jameson A, Smyth B (2007) Recommendation to groups. In: The adaptive web, pp 596–627. Berlin Heidelberg: Springer

  • Xu B, Deng L, Jia, Y, Zhou B, Han Y (2013) Social circle analysis on ego-network based on context frequent pattern mining. In: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service, pp 139–144. ACM

  • Kant V, Bharadwaj KK (2012) Enhancing recommendation quality of content-based filtering through collaborative predictions and fuzzy similarity measures. Procedia Eng 38:939–944

    Article  Google Scholar 

  • Kim KJ, Ahn H (2008) A recommender system using GA K-means clustering in an online shopping market. Expert Syst Appl 34(2):1200–1209

    Article  Google Scholar 

  • Kim JK, Kim HK, Oh HY, Ryu YU (2010) A group recommendation system for online communities. Int J Inf Manage 30(3):212–219

    Article  Google Scholar 

  • Leskovec J, Huttenlocher D, Kleinberg J (2010) Signed networks in social media. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 1361–1370. ACM

  • Lieberman H, Van Dyke N, Vivacqua A (1999) Let’s browse: a collaborative browsing agent. Knowl-Based Syst 12(8):427–431

    Article  Google Scholar 

  • Liu T, Qin H (2016) Detecting and tagging users’ social circles in social media. Multimedia Syst 22(4):423–431

    Article  Google Scholar 

  • MacLean D, Hangal S, Teh SK, Lam MS, Heer J (2011) Groups without tears: mining social topologies from email. In: Proceedings of the 16th international conference on Intelligent user interfaces, pp 83–92

  • Massa P, Avesani P (2006) Trust-aware bootstrapping of recommender systems. In: ECAI workshop on recommender systems, pp 29–33

  • Masthoff J (2004) Group modeling: Selecting a sequence of television items to suit a group of viewers. In: Personalized digital television, pp 93–141. Springer, Dordrecht

  • McAuley J, Leskovec J (2014) Discovering social circles in ego networks. ACM Trans Knowl Discov Data (TKDD) 8(1):4

    Google Scholar 

  • McCarthy JF (2002) Pocket restaurant finder: A situated recommender system for groups. In: Workshop on Mobile Ad-Hoc Communication at the 2002 ACM Conference on Human Factors in Computer Systems, p 8

  • McCarthy JF, Anagnost TD (1998) MusicFX: an arbiter of group preferences for computer supported collaborative workouts. In: Proceedings of the 1998 ACM conference on Computer supported cooperative work, pp 363–372. ACM

  • McCarthy K, McGinty L, Smyth B, Salamó M (2006) The needs of the many: a case-based group recommender system. In: European Conference on Case-Based Reasoning, pp 196–210. Springer, Berlin, Heidelberg

  • O’connor M, Cosley D, Konstan JA, Riedl J (2001) PolyLens: a recommender system for groups of users. In: ECSCW 2001, pp 199–218. Netherlands: Springer

  • Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814

    Article  Google Scholar 

  • Qi GJ, Aggarwal CC, Huang T (2012) Community detection with edge content in social media networks. In: 2012 IEEE 28th International Conference on Data Engineering, pp 534–545. IEEE

  • Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B, Jimenez-Diaz G (2013) Social factors in group recommender systems. ACM Trans Intell Syst Technol (TIST) 4(1):8

    Google Scholar 

  • Recio-Garcia JA, Jimenez-Diaz G, Sanchez-Ruiz AA, Diaz-Agudo B (2009) Personality aware recommendations to groups. In: Proceedings of the third ACM conference on Recommender systems, pp 325–328. ACM

  • Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3):56–58

    Article  Google Scholar 

  • Ricci F, Cavada D, Nguyen QN (2002) Integrating travel planning and on-tour support in a case-based recommender system. In: Proceedings of the Workshop on Mobile Tourism Systems, pp 11–16

  • Sahebi S, Cohen WW (1997) Community-based recommendations: a solution to the cold start problem. In: Proceedings of WOODSTOCK’97

  • Sinha RR, Swearingen K (2001) Comparing recommendations made by online systems and friends. In: DELOS

  • Symeonidis P, Tiakas E (2014) Transitive node similarity: predicting and recommending links in signed social networks. World Wide Web 17(4):743–776

    Article  Google Scholar 

  • Villavicencio C, Schiaffino S, Diaz-Pace JA, Monteserin A, Demazeau Y, Adam C (2016) A MAS approach for group recommendation based on negotiation techniques. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp 219–231. Springer, Cham

  • Yang X, Steck H, Liu Y (2012) Circle-based recommendation in online social networks. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1267–1275. ACM

  • Yu Z, Zhou X, Hao Y, Gu J (2006) TV program recommendation for multiple viewers based on user profile merging. User Model User-Adap Inter 16(1):63–82

    Article  Google Scholar 

Download references

Funding

This study is not funded by any organization.

Author information

Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    Nirmal Choudhary, Sonajharia Minz & K. K. Bharadwaj

Authors
  1. Nirmal Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sonajharia Minz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. K. Bharadwaj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nirmal Choudhary.

Ethics declarations

Conflict of interest

Nirmal Choudhary declares that she has no conflict of interest. Sonajharia Minz declares that she has no conflict of interest. K. K. Bharadwaj declares that he has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Choudhary, N., Minz, S. & Bharadwaj, K.K. Circle-based Group Recommendation in Social Networks. Soft Comput 25, 13959–13973 (2021). https://doi.org/10.1007/s00500-020-05356-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05356-y

Keywords

Search

Navigation