Analysis of Online Product Purchase and Predicting Items for Co-purchase

  • Sohom Ghosh
  • Angan Mitra
  • Partha Basuchowdhuri
  • Sanjoy Kumar Saha
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 43)

Abstract

In recent years, online market places have become popular among the buyers. During this course of time, not only have they sustained the business model but also generated large amount of profit, turning it into a lucrative business model. In this paper, we take a look at a temporal dataset from one of the most successful online businesses to analyze the nature of the buying patterns of the users. Arguably, the most important purchase characteristic of such networks is follow-up purchase by a buyer, otherwise known as a co-purchase. In this paper, we also analyze the co-purchase patterns to build a knowledge-base to recommend potential co-purchase items for every item.

Keywords

Viral marketing Dynamic networks Social networks Recommendation system Amazon co-purchase networks 

References

  1. 1.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: “The dynamics of viral marketing. ACM Trans. Web 1(1), (2007)Google Scholar
  2. 2.
    Oestreicher-Singer, G., Sundararajan, A.: Linking network structure to e-commerce demand: theory and evidence from amazon.coms copurchase network. In: TPRC 2006. Available in SSRN (2006)Google Scholar
  3. 3.
    Newman, M.: Modularity and community structure in networks. Proc. Nat. Acad. Sci. 103(23), 85778582 (2006)Google Scholar
  4. 4.
    Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)Google Scholar
  5. 5.
    Luo, F., Wang, J.Z., Promislow, E.: Exploring local community structures in large networks. Web Intell. Agent Syst. 6(4), 387400 (2008)Google Scholar
  6. 6.
    Srivastava, A.: Motif analysis in the amazon product co-purchasing network. In: CoRR, vol. abs/1012.4050 (2010)Google Scholar
  7. 7.
    Bogdanov, P., Mongiov, M., Singh, A.K.: Mining heavy subgraphs in time-evolving networks. In: Cook, D.J., Pei, J., 0010, W.W., Zaane, O.R., Wu, X. (eds.) ICDM, pp. 8190. IEEE (2011)Google Scholar
  8. 8.
    Lahiri, M., Berger-Wolf, T.Y.: Structure prediction in temporal networks using frequent subgraphs. In: CIDM, p. 3542. IEEE (2007)Google Scholar
  9. 9.
    Wackersreuther, B., Wackersreuther, P., Oswald, A., Bohm, C., Borgwardt, K.M.: Frequent subgraph discovery in dynamic networks. In: Proceedings of the Eighth Workshop on Mining and Learning with Graphs, ser. MLG 10, pp. 155–162. ACM, New York (2010)Google Scholar
  10. 10.
    Lahiri, M., Berger-Wolf, T.Y.: Periodic subgraph mining in dynamic networks. Knowl. Inf. Syst. 24(3), 467497 (2010)Google Scholar
  11. 11.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 5387 (2004)Google Scholar
  12. 12.
    Bron, C., Kerbosch, J.: Algorithm 457: finding all cliques of an undirected graph. Commun. ACM 16(9), 575–577 (1973)Google Scholar
  13. 13.
    Basuchowdhuri, P., Shekhawat, M.K., Saha, S.K.: Analysis of Product Purchase Patterns in a Co-purchase Network. EAIT (2014)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  • Sohom Ghosh
    • 1
  • Angan Mitra
    • 2
  • Partha Basuchowdhuri
    • 1
  • Sanjoy Kumar Saha
    • 2
  1. 1.Department of Computer Science and EngineeringHeritage Institute of TechnologyKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

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