---
_id: '5671'
abstract:
- lang: eng
  text: Multi-attribute value theory (MAVT)-based recommender systems have been proposed
    for dealing with issues of existing recommender systems, such as the cold-start
    problem and changing preferences. However, as we argue in this paper, existing
    MAVT-based methods for measuring attribute importance weights do not fit the shopping
    tasks for which recommender systems are typically used. These methods assume well-trained
    decision makers who are willing to invest time and cognitive effort, and who are
    familiar with the attributes describing the available alternatives and the ranges
    of these attribute levels. Yet, recommender systems are most often used by consumers
    who are usually not familiar with the available attributes and ranges and who
    wish to save time and effort. Against this background, we develop a new method,
    based on a product configuration process, which is tailored to the characteristics
    of these particular decision makers. We empirically compare our method to SWING,
    ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory
    experiment with 153 participants. Results indicate that our proposed method performs
    better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation
    accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms
    of cognitive load, and that participants were faster with our method than with
    any other method. We conclude that our method is a promising option to help support
    consumers' decision processes in e-commerce shopping tasks.
author:
- first_name: Michael
  full_name: Scholz, Michael
  last_name: Scholz
- first_name: Verena
  full_name: Dorner, Verena
  last_name: Dorner
- first_name: Guido
  full_name: Schryen, Guido
  id: '72850'
  last_name: Schryen
- first_name: Alexander
  full_name: Benlian, Alexander
  last_name: Benlian
citation:
  ama: Scholz M, Dorner V, Schryen G, Benlian A. A configuration-based recommender
    system for supporting e-commerce decisions. <i>European Journal of Operational
    Research</i>. 2017;259(1):205-215.
  apa: Scholz, M., Dorner, V., Schryen, G., &#38; Benlian, A. (2017). A configuration-based
    recommender system for supporting e-commerce decisions. <i>European Journal of
    Operational Research</i>, <i>259</i>(1), 205–215.
  bibtex: '@article{Scholz_Dorner_Schryen_Benlian_2017, title={A configuration-based
    recommender system for supporting e-commerce decisions}, volume={259}, number={1},
    journal={European Journal of Operational Research}, publisher={Elsevier}, author={Scholz,
    Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander}, year={2017},
    pages={205–215} }'
  chicago: 'Scholz, Michael, Verena Dorner, Guido Schryen, and Alexander Benlian.
    “A Configuration-Based Recommender System for Supporting e-Commerce Decisions.”
    <i>European Journal of Operational Research</i> 259, no. 1 (2017): 205–15.'
  ieee: M. Scholz, V. Dorner, G. Schryen, and A. Benlian, “A configuration-based recommender
    system for supporting e-commerce decisions,” <i>European Journal of Operational
    Research</i>, vol. 259, no. 1, pp. 205–215, 2017.
  mla: Scholz, Michael, et al. “A Configuration-Based Recommender System for Supporting
    e-Commerce Decisions.” <i>European Journal of Operational Research</i>, vol. 259,
    no. 1, Elsevier, 2017, pp. 205–15.
  short: M. Scholz, V. Dorner, G. Schryen, A. Benlian, European Journal of Operational
    Research 259 (2017) 205–215.
date_created: 2018-11-14T15:06:18Z
date_updated: 2022-01-06T07:02:27Z
ddc:
- '000'
department:
- _id: '277'
extern: '1'
file:
- access_level: open_access
  content_type: application/pdf
  creator: hsiemes
  date_created: 2018-12-07T11:30:59Z
  date_updated: 2018-12-13T15:06:56Z
  file_id: '6025'
  file_name: EJOR article.pdf
  file_size: 762889
  relation: main_file
file_date_updated: 2018-12-13T15:06:56Z
has_accepted_license: '1'
intvolume: '       259'
issue: '1'
keyword:
- E-Commerce
- Recommender System
- Attribute Weights
- Configuration System
- Decision Support
language:
- iso: eng
oa: '1'
page: 205 - 215
publication: European Journal of Operational Research
publisher: Elsevier
status: public
title: A configuration-based recommender system for supporting e-commerce decisions
type: journal_article
user_id: '61579'
volume: 259
year: '2017'
...