Skip to main content
SpringerLink
Log in

Promotional Predictive Marketing: User Centric Data Driven Approach

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

This paper proposes value addition to the classical Influence Maximization problem by introducing a quality measure to the participating nodes. The quality measure signifies the ‘propensity to buy’ of a customer (node) in a promotional marketing campaign context. Two metrics, Individual Net Worth (INW) and Neighborhood Net Worth (NNW) are proposed to measure the potential of a customer(s) in buying a given product. The proposed solution, through a heuristic approach, is capable of spreading the influence to the customers with a higher propensity to buy the product. The solution is scalable and adaptable to address user requirements. All these claims are substantiated through experimental results on public datasets. We performed a comparative study with notable algorithms in this domain. The result shows that the proposed approach selects seeds of higher quality as well as maximizes the overall quality (worth) of the influenced nodes in comparison to the notable algorithms, without any adverse impact on time complexity.

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
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of Data and Material

Public datasets are used. The sources of the datasets are mentioned in the dataset Sect. 6.1 in the manuscript.

Code Availability

Custom code developed using python for experimental validation.

References

  1. Alduaiji N, Datta A, Li J. Influence propagation model for clique-based community detection in social networks, digital object identifier. IEEE Trans Comput Soc Syst. 2018;5(2):563–75. https://doi.org/10.1109/TCSS.2018.2831694.

    Article  Google Scholar 

  2. Alp ZZ, Şule Gündüz Ö. Influence factorization for identifying authorities in Twitter. Knowl Based Syst. 2019;163:944–54. https://doi.org/10.1016/j.knosys.2018.10.020.

    Article  Google Scholar 

  3. Chen S, Fan J, Li G, Feng J, Tan K, Tang J. Online topic-aware influence maximization. In: Proceedings of the VLDB endowment, ACM, vol. 8, no. 6, pp. 666–677, 2015. DOI:https://doi.org/10.14778/2735703.2735706.

  4. Chen W, Wang Y, Yang S. Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD, KDD’09, pp. 199–208. ACM, New York, NY. https://doi.org/10.1145/1557019.1557047.

  5. Cui L, Hu H, Yu S, Yan Q, Ming Z, Wen Z, Lu N. DDSE: a novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks. J Netw Comput Appl. 2018;103:119–30. https://doi.org/10.1016/j.jnca.2017.12.003.

    Article  Google Scholar 

  6. Goyal A, Lu W, Lakshmanan LV (2011) Celf++: optimizing the greedy algorithm for influence maximization in social networks. In: Proceedings of the 20th international conference companion on World wide web, ACM, 2011, https://doi.org/10.1145/1963192.1963217.

  7. Leskovec J, Krause A, Guestrin CE, Faloutsos C, VanBriesen J, Glance NS. Cost-effective outbreak detection in networks. In: KDD '07, Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining, p 420–429, August 2007. https://doi.org/10.1145/1281192.

  8. Kempe D, Kleinberg J, Tordas E. Maximizing the spread of influence through social network. Proc ACM SIG Knowl Discov Datamin. 2003. https://doi.org/10.1145/956750.956769.

    Article  Google Scholar 

  9. Kundu G, Choudhury S. A discrete genetic learning enabled PSO for targeted positive influence maximization in consumer review networks. Innov Syst Softw Eng. 2021;17:247–59. https://doi.org/10.1007/s11334-021-00396-5.

    Article  Google Scholar 

  10. Rossi M-EG, Shi B, Tziortziotis N, Malliaros FD, Giatsidis C, Vazirgiannis M. MATI: an efficient algorithm for influence maximization in social networks. PLoS One. 2018;13(11):e0206318. https://doi.org/10.1371/journal.pone.0206318.

    Article  Google Scholar 

  11. Singh SS, Kumar A, Singh K, Biswas B. C2IM: community based context-aware influence maximization in social networks. Physica A Stat Mech Appl. 2019;514:796–818. https://doi.org/10.1016/j.physa.2018.09.142.

    Article  MathSciNet  MATH  Google Scholar 

  12. Sumith N, Annappa B, Bhattacharya S. A holistic approach to influence maximization. Hybrid intelligence for social networks. Springer, 2017. https://doi.org/10.1016/j.asoc.2017.12.025

  13. Sumith N, Annappa B, Bhattacharya S. Influence maximization in large social networks: heuristics, models and parameters. Future Gener Comput Syst. 2018;89:777–90. https://doi.org/10.1016/j.future.2018.07.015.

    Article  Google Scholar 

  14. Xu X, Zhang Y, Hu Q, Xing C. A balanced method for budgeted influence maximization. In: SEKE (2015), pp. 250–255, 2015. https://doi.org/10.18293/SEKE2015-157.

  15. Guoliang Li, Shuo Chen, Jianhua Feng, Kian-lee Tan, and Wen-syan Li, (2014), “Efficient location-aware influence maximization”, Proceedings of ACM SIGMOD International Conference on Management of Data (SIGMOD '14). ACM, NY, USA, pp. 87–98. 2014, DOI:https://doi.org/10.1145/2588555.2588561

  16. Zareie A, Sheikhahmadi A, Khamforoosh K. Influence maximization in social networks based on TOPSIS. Expert Syst Appl. 2018;108(15):96–107. https://doi.org/10.1016/j.eswa.2018.05.001.

    Article  Google Scholar 

  17. Li L, Liu Y, Zhou Q, Yang W, Yuan J. Targeted influence maximization under a multifactor-based information propagation model. Inf Sci. 2020;519:124–40. https://doi.org/10.1016/j.ins.2020.01.040.

    Article  MathSciNet  Google Scholar 

  18. Jiuxin C, Tao Z, Dan D, Shuai X, Ziqing Z, Ma Z, Bo L. Attribute-based influence maximization in social networks. In: Web information systems engineering—WISE 2016, vol. 10041, pp. 3–18, 2016. https://doi.org/10.1007/978-3-319-48740-3_1.

  19. Chanda J, Kanjilal A, Sengupta S, Bhattacharya S. Traceability of requirements and consistency verification of UML use case, activity and class diagram: a formal approach. In: Proceedings of international conference on methods and models in computer science (ICM2CS), pp. 1–4, 2009. https://doi.org/10.1109/ICM2CS.2009.5397941.

  20. Sengupta S, Kanjilal A, Bhattacharya S (2008) Requirement traceability in software development process: an empirical approach. In: 2008 The 19th IEEE/IFIP international symposium on rapid system prototyping, pp. 105–111, 2008. https://doi.org/10.1109/RSP.2008.14.

  21. Kanjilal A, Sengupta S, Bhattacharya S. Analysis of complexity of requirements: a metrics based approach. In: Proceedings of the 2nd India software engineering conference (ISEC'09), pp. 131–132, 2009, https://doi.org/10.1145/1506216. 1506243.

  22. Domingos P, Richardson M. Mining the network value of customers. In: Proceedings of the seventh ACM international conference on knowledge discovery and data mining (SIGKDD), pp. 57–66, 2001, https://doi.org/10.1145/502512.502525

  23. Social circles: Facebook. https://snap.stanford.edu/data/ego-Facebook.html. Accessed 15 May 2021.

  24. High Energy Physics—theory collaboration network, https://snap.stanford.edu/data/ca-HepTh.html. Accessed 15 May 2021.

  25. High Energy Physics Theory—NetHept dataset. http://www.cs.cornell.edu/projects/kddcup/datasets.html. Accessed 10 May 2021.

Download references

Funding

This study was not funded.

Author information

Authors and Affiliations

  1. Department of CSE, BPPIMT, Kolkata, India

    Soumi Tokdar & Ananya Kanjilal

  2. Department of CSE, University of Calcutta, Kolkata, India

    Sankhayan Choudhury

  3. Deparment of CSE, Jadavpur University, Kolkata, India

    Swapan Bhattacharya

Authors
  1. Soumi Tokdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ananya Kanjilal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sankhayan Choudhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Swapan Bhattacharya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Soumi Tokdar.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Appendix 1: Comparison of seed node set for varying weight values

Appendix 1: Comparison of seed node set for varying weight values

W (weights for diff attributes)

Algorithm

Seed set (Node numbers)

Diff. in seeds

Diff %

W1 = (0,0,0.33,0.67)

AWMIS (proposed approach)

1441, 3423, 6142, 6948, 7859, 13648, 14017, 14726, 16164, 19615, 23282, 23420, 24059, 26122, 28950, 29928, 30160, 30744, 33512, 34787, 36103, 39085, 44262, 53601, 54465, 63113, 63697, 65168, 65553, 68111

Benchmark for comparison

W2 = (0,0.5,0.5,0)

97, 1441, 4436, 6142, 6948, 7859, 13648, 14726, 17289, 17370, 19615, 23282, 23420, 26122, 28950, 29928, 30744, 33512, 34787, 36103, 39085, 44262, 54465, 60926, 63113, 63697, 65168, 65553, 66349, 68111

6

20%

W3 = (0.33,0.67,0,0)

1441, 3423, 4436, 6142, 6948, 7233, 7859, 13648, 14017, 14726, 16164, 17370, 17793, 19615, 23282, 23420, 26122, 28950, 30160, 30744, 33512, 36103, 39085, 44262, 53601, 54465, 63113, 63697, 65168, 68111

4

13.33%

W4 = (0.5,0,0, 0.5)

1441, 3423, 4436, 6142, 6948, 7859, 13648, 14017, 14726, 16164, 17370, 17793, 19615, 23282, 23420, 26122, 28950, 30160, 30744, 33512, 36103, 39085, 44262, 48973, 53601, 54465, 63113, 63697, 65168, 68111

4

13.33%

W1 = (0,0,0.33,0.67)

CELF

97, 1441, 3423, 4436, 6142, 6948, 7859, 14017, 14726, 16164, 17370, 17793, 19615, 20394, 23282, 23420, 28950, 29715, 30744, 33512, 36103, 39085, 43684, 44262, 54465, 62227, 63113, 63697, 65168, 68111

No change

W2, W3, W4

Same seed set. No change due to change in W

W1, W2, W3, W4

DD

Same seed set. No change due to change in W

No change

W1, W2, W3, W4

ORIE

Same seed set. No change due to change in W

No change

  1. *Nodes in bold are new nodes selected in the seed set replacing the old ones based on varying W values

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tokdar, S., Kanjilal, A., Choudhury, S. et al. Promotional Predictive Marketing: User Centric Data Driven Approach. SN COMPUT. SCI. 3, 444 (2022). https://doi.org/10.1007/s42979-022-01342-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-022-01342-3

Keywords

Search

Navigation