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Recommendations Based on Collective Intelligence – Case of Customer Segmentation

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Book cover Information Technology for Management: Emerging Research and Applications (AITM 2018, ISM 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 346))

Abstract

The article discusses the usage and benefits of the recommendation systems based on data mining mechanisms targeting e-commerce industry. In particular the article focuses on the idea of collective clustering to perform customer segmentation. Results of many clustering algorithms in segmentation inspired by the RFM method are presented. The positive business-oriented outcomes of collective clustering are demonstrated on real-live marketing databases.

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Notes

  1. 1.

    Upsaily system was developed by the Unity S.A., Wrocław, in the framework of the Real-Time Omnichannel Marketing (RTOM) project, RPO WD 2014-2020.

  2. 2.

    The Davies-Bouldin index is computed according to the formula: \( DB = 0.5n\,\varSigma \,max\,(\left( {si + sj} \right)/d\left( {ci,cj} \right) \) where n is the number of clusters, the cluster centroids, si and sj mean d distances between the elements of a given cluster and the centroid. The algorithm that generates the smallest value of the DB indicator is considered the best according to the criterion of internal evaluation.

  3. 3.

    The Dunn index is calculated according to the formula: \( D = min(d\left( {i,j} \right)/max\,d^{{\prime }} \left( k \right) \) where d(i, j) means the distance between clusters i i j and d’(k) the measure of distances within the cluster k. The Dunn index focuses on cluster density and distances between cluster. Preferred algorithms according to the Dunn index are those that achieve high index values.

  4. 4.

    The HDBSCAN algorithm, which is an extension of the DBSCAN algorithm, was used. A library available on the GitHub platform was used for this purpose: https://hdbscan.readthedocs.io/en/latest/index.html

  5. 5.

    In brief, the purpose of the PCA method is to find a linear subspace (in our case 2-dimensional) in which the variance after projection remains the largest. However, the PCA method should not easily reject the dimensions with the lowest variance. It builds a new coordinates system in which the remaining values are the most diverse.

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Author information

Authors and Affiliations

  1. Wrocław University of Economics, Komandorska 118/120, Wrocław, Poland

    Maciej Pondel

  2. International School of Logistics and Transport, Sołtysowicka St. 19 B, 51-168, Wrocław, Poland

    Jerzy Korczak

Authors
  1. Maciej Pondel
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  2. Jerzy Korczak
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Corresponding authors

Correspondence to Maciej Pondel or Jerzy Korczak .

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Editors and Affiliations

  1. University of Economics in Katowice, Katowice, Poland

    Ewa Ziemba

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Pondel, M., Korczak, J. (2019). Recommendations Based on Collective Intelligence – Case of Customer Segmentation. In: Ziemba, E. (eds) Information Technology for Management: Emerging Research and Applications. AITM ISM 2018 2018. Lecture Notes in Business Information Processing, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-15154-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-15154-6_5

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