poster

Increasing consumers' understanding of recommender results: a preference-based hybrid algorithm with strong explanatory power

Authors:

Thorsten Hennig-Thurau,

André MarchandAuthors Info & Claims

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Pages 297 - 300

https://doi.org/10.1145/1864708.1864771

Published: 26 September 2010 Publication History

Abstract

Recommender systems are intended to assist consumers by making choices from a large scope of items. While most recommender research focuses on improving the accuracy of recommender algorithms, this paper stresses the role of explanations for recommended items for gaining acceptance and trust. Specifically, we present a method which is capable of providing detailed explanations of recommendations while exhibiting reasonable prediction accuracy. The method models the users' ratings as a function of their utility part-worths for those item attributes which influence the users' evaluation behavior, with part-worth being estimated through a set of auxiliary regressions and constrained optimization of their results. We provide evidence that under certain conditions the proposed method is superior to established recommender approaches not only regarding its ability to provide detailed explanations but also in terms of prediction accuracy. We further show that a hybrid recommendation algorithm can rely on the content-based component for a majority of the users, switching to collaborative recommendation only for about one third of the user base.

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Cited By

Yıldız EGüngör Şen CIşık E(2023)A Hyper-Personalized Product Recommendation System Focused on Customer Segmentation: An Application in the Fashion Retail IndustryJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1801002918:1(571-596)Online publication date: 11-Mar-2023
https://doi.org/10.3390/jtaer18010029
Hennig-Thurau THouston MHennig-Thurau THouston M(2018)Entertainment Communication Decisions, Episode 2: “Earned” ChannelsEntertainment Science10.1007/978-3-319-89292-4_12(587-677)Online publication date: 1-Aug-2018
https://doi.org/10.1007/978-3-319-89292-4_12
Bellogín ACastells PCantador I(2014)Neighbor Selection and Weighting in User-Based Collaborative FilteringACM Transactions on the Web10.1145/25799938:2(1-30)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1145/2579993

Index Terms

Increasing consumers' understanding of recommender results: a preference-based hybrid algorithm with strong explanatory power
1. Information systems
  1. Information retrieval
  2. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

September 2010

402 pages

ISBN:9781605589060

DOI:10.1145/1864708

General Chairs:
Xavier Amatriain
Telefonica Research, Spain
,
Marc Torrens
Strands, Spain
,
Program Chairs:
Paul Resnick
University of Michigan, USA
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria

Copyright © 2010 ACM.

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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New York, NY, United States

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Published: 26 September 2010

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RecSys '10

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SIGCHI

RecSys '10: Fourth ACM Conference on Recommender Systems

September 26 - 30, 2010

Barcelona, Spain

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Yıldız EGüngör Şen CIşık E(2023)A Hyper-Personalized Product Recommendation System Focused on Customer Segmentation: An Application in the Fashion Retail IndustryJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1801002918:1(571-596)Online publication date: 11-Mar-2023
https://doi.org/10.3390/jtaer18010029
Hennig-Thurau THouston MHennig-Thurau THouston M(2018)Entertainment Communication Decisions, Episode 2: “Earned” ChannelsEntertainment Science10.1007/978-3-319-89292-4_12(587-677)Online publication date: 1-Aug-2018
https://doi.org/10.1007/978-3-319-89292-4_12
Bellogín ACastells PCantador I(2014)Neighbor Selection and Weighting in User-Based Collaborative FilteringACM Transactions on the Web10.1145/25799938:2(1-30)Online publication date: 1-Mar-2014
https://dl.acm.org/doi/10.1145/2579993

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