Automated Product Recommendations with Preference-Based Explanations

André Marchand and Paul Marx

Journal of Retailing, 2020, vol. 96, issue 3, 328-343

Abstract: Many online retailers, such as Amazon, use automated product recommender systems to encourage customer loyalty and cross-sell products. Despite significant improvements to the predictive accuracy of contemporary recommender system algorithms, they remain prone to errors. Erroneous recommendations pose potential threats to online retailers in particular, because they diminish customers’ trust in, acceptance of, satisfaction with, and loyalty to a recommender system. Explanations of the reasoning that lead to recommendations might mitigate these negative effects. That is, a recommendation algorithm ideally would provide both accurate recommendations and explanations of the reasoning for those recommendations. This article proposes a novel method to balance these concurrent objectives. The application of this method, using a combination of content-based and collaborative filtering, to two real-world data sets with more than 100 million product ratings reveals that the proposed method outperforms established recommender approaches in terms of predictive accuracy (more than five percent better than the Netflix Prize winner algorithm according to normalized root mean squared error) and its ability to provide actionable explanations, which is also an ethical requirement of artificial intelligence systems.

Keywords: Recommender systems; Recommendation explanations; Decision support systems; Consumer preferences; Netflix; MoviePilot (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0022435920300014
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jouret:v:96:y:2020:i:3:p:328-343

DOI: 10.1016/j.jretai.2020.01.001

Access Statistics for this article

Journal of Retailing is currently edited by A. Roggeveen

More articles in Journal of Retailing from Elsevier
Bibliographic data for series maintained by Catherine Liu ().