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Integrating tradeoff support in product search tools for e-commerce sites

Published: 05 June 2005 Publication History

Abstract

In a previously reported user study, we found that users were able to perform decision tradeoff tasks more efficiently and commit considerably fewer errors with the example critiquing interface than with the ranked list. We concluded that example-based search tools were likely to be useful particularly for extending the scope of consumer e-commerce to more complex products where decision making is critical. This paper presents results from a follow-up user study quantifying the benefits of tradeoff support. Users were able to refine the quality of their preference structures and improve decision accuracy by up to 57% after performing tradeoff tasks. Tradeoff support also significantly increased users' confidence in their choices. Together, these two studies show that example critiquing enables users to more accurately find what they want and be confident in their choices, while only requiring a level of effort that is comparable to the ranked list interface.

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cover image ACM Conferences
EC '05: Proceedings of the 6th ACM conference on Electronic commerce
June 2005
302 pages
ISBN:1595930493
DOI:10.1145/1064009
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 05 June 2005

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Author Tags

  1. SmartClient systems
  2. consumer e-commerce
  3. decision support
  4. empirical user study
  5. example critiquing interactions

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EC05
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EC05: Sixth ACM Conference on Electronic Commerce 2005
June 5 - 8, 2005
BC, Vancouver, Canada

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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  • (2023)User Experience and the Role of Personalization in Critiquing-Based Conversational RecommendationACM Transactions on the Web10.1145/359749918:4(1-21)Online publication date: 18-May-2023
  • (2022)Effects of Feature-Based Explanation and Its Output Modality on User Satisfaction With Service Recommender SystemsFrontiers in Big Data10.3389/fdata.2022.8973815Online publication date: 6-May-2022
  • (2019)User Evaluations on Sentiment-based Recommendation ExplanationsACM Transactions on Interactive Intelligent Systems10.1145/32828789:4(1-38)Online publication date: 9-Aug-2019
  • (2018)Evaluation of a search interface for preference-based rankingProceedings of the 10th Nordic Conference on Human-Computer Interaction10.1145/3240167.3240170(184-194)Online publication date: 29-Sep-2018
  • (2017)Investigating users’ eye movement behavior in critiquing-based recommender systemsAI Communications10.3233/AIC-17073730:3-4(207-222)Online publication date: 1-Jan-2017
  • (2016)An Eye-Tracking StudyProceedings of the 2016 Conference on User Modeling Adaptation and Personalization10.1145/2930238.2930286(163-167)Online publication date: 13-Jul-2016
  • (2015)Recommender systems based on user reviewsUser Modeling and User-Adapted Interaction10.1007/s11257-015-9155-525:2(99-154)Online publication date: 1-Jun-2015
  • (2015)Human Decision Making and Recommender SystemsRecommender Systems Handbook10.1007/978-1-4899-7637-6_18(611-648)Online publication date: 2015
  • (2013)Preference-based clustering reviews for augmenting e-commerce recommendationKnowledge-Based Systems10.5555/2770959.277106950:C(44-59)Online publication date: 1-Sep-2013
  • (2013)Human Decision Making and Recommender SystemsACM Transactions on Interactive Intelligent Systems10.1145/2533670.25336753:3(1-7)Online publication date: 1-Oct-2013
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