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International Conference on e-Business Engineering

ICEBE 2019: Advances in E-Business Engineering for Ubiquitous Computing pp 161–172Cite as

A Customer-Oriented Assortment Selection in the Big Data Environment

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Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 41))

Abstract

Customers prefer the availability of a range of products when they shop online. This enables them to identify their needs and select products that best match their desires. This is addressed through assortment planning. Some customers have strong awareness of what they want to purchase and from which provider. When considering customer taste as an abstract concept, such customers’ decisions may be influenced by the existence of the variety of products and the current variant market may affect their initial desire. Previous studies dealing with assortment planning have commonly addressed it from the retailer’s point of view. This paper will provide customers with a ranking method to find what they want. We propose that this provision benefits both the retailer and the customer. This study provides a customer-oriented assortment ranking approach. The ranking model facilitates browsing and exploring the current big market in order to help customers find their desired item considering their own taste. In this study, a scalable and customised multi-criteria decision making (MCDM) method is structured and utilised to help customers in the process of finding their most suitable assortment while shopping online. The proposed MCDM method is tailored to fit the big data environment.

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Author information

Authors and Affiliations

  1. School of Information, Systems and Modelling, UTS, Sydney, Australia

    Morteza Saberi

  2. School of Business, UNSW Canberra, Canberra, Australia

    Zahra Saberi, Mehdi Rajabi Aasadabadi, Omar Khadeer Hussain & Elizabeth Chang

  3. Australian National University, Canberra, ACT, 0200, Australia

    Mehdi Rajabi Aasadabadi

Authors
  1. Morteza Saberi
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  2. Zahra Saberi
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  3. Mehdi Rajabi Aasadabadi
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  4. Omar Khadeer Hussain
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  5. Elizabeth Chang
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Corresponding author

Correspondence to Morteza Saberi .

Editor information

Editors and Affiliations

  1. ECB, Faculty of Engineering and Computing, Coventry University, Coventry, UK

    Kuo-Ming Chao

  2. School of Software, Shanghai Jiaotong University, Shanghai, China

    Lihong Jiang

  3. School of Business, University of New South Wales, Canberra, ACT, Australia

    Omar Khadeer Hussain

  4. National Taiwan Ocean University, Keelung City, Taiwan

    Shang-Pin Ma

  5. Faculty of Engineering and Computing, Coventry University, Coventry, UK

    Xiang Fei

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Saberi, M., Saberi, Z., Aasadabadi, M.R., Hussain, O.K., Chang, E. (2020). A Customer-Oriented Assortment Selection in the Big Data Environment. In: Chao, KM., Jiang, L., Hussain, O., Ma, SP., Fei, X. (eds) Advances in E-Business Engineering for Ubiquitous Computing. ICEBE 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-030-34986-8_11

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