Personalization without Interrogation: Towards more Effective Interactions between Consumers and Feature-Based Recommendation Agents
Kyle B. Murray and
Gerald Häubl
Journal of Interactive Marketing, 2009, vol. 23, issue 2, 138-146
Abstract:
Software agents that provide consumers with personalized product recommendations based on individual-level feature-based preference models have been shown to facilitate better consumption choices while dramatically reducing the effort required to make these choices. This article examines why, despite their usefulness, such tools have not yet been widely adopted in the marketplace. We argue that the primary reason for this is that the usability of recommendation systems has been largely neglected – both in academic research and in practice – and we outline a roadmap for future research that might lead to recommendation agents that are more readily adopted by consumers.
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1094996809000334
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:joinma:v:23:y:2009:i:2:p:138-146
DOI: 10.1016/j.intmar.2009.02.009
Access Statistics for this article
Journal of Interactive Marketing is currently edited by B. T. Ratchford
More articles in Journal of Interactive Marketing from Elsevier
Bibliographic data for series maintained by Catherine Liu ().