---
_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. European Journal of Operational
Research. 2017;259(1):205-215.
apa: Scholz, M., Dorner, V., Schryen, G., & Benlian, A. (2017). A configuration-based
recommender system for supporting e-commerce decisions. European Journal of
Operational Research, 259(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.”
European Journal of Operational Research 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,” European Journal of Operational
Research, vol. 259, no. 1, pp. 205–215, 2017.
mla: Scholz, Michael, et al. “A Configuration-Based Recommender System for Supporting
e-Commerce Decisions.” European Journal of Operational Research, 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'
...