--- _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' ...