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User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithms

Authors:

Johannes Nakayama,

Nils Plettenberg,

Martina Ziefle,

André Calero ValdezAuthors Info & Claims

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Pages 306 - 310

https://doi.org/10.1145/3240323.3240393

Published: 27 September 2018 Publication History

Abstract

The use of recommendation systems is widespread in online commerce. Depending on the algorithm that is used in the recommender system different types of data are recorded from user interactions. Typically, better recommendations are achieved when more detailed data about the user and product is available. However, users are often unaware of what data is stored and how it is used in recommendation. In a survey study with 197 participants we introduced different recommendation techniques (collaborative filtering, content-based recommendation, trust-based and social recommendation) to the users and asked participants to rate what type of algorithm should be used for what type of product category (books, mobile phones, contraceptives). We found different patterns of preferences for different product categories. The more sensitive the product the higher the preference for content-based filtering approaches that could work without storing personal data. Trust-based and social approaches utilizing data from social media were generally rejected.

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Cited By

Tachioka Y(2024)User Knowledge Prompt for Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691714(1142-1146)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691714
Feng KKoo XTan LBruckman AMcDonald DZhang A(2024)Mapping the Design Space of Teachable Social Media Feed ExperiencesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642120(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642120
Muhammad RKasahara S(2024)Agent-based simulation of fake news dissemination: the role of trust assessment and big five personality traits on news spreadingSocial Network Analysis and Mining10.1007/s13278-024-01235-814:1Online publication date: 30-Mar-2024
https://doi.org/10.1007/s13278-024-01235-8
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Index Terms

User preferences in recommendation algorithms: the influence of user diversity, trust, and product category on privacy perceptions in recommender algorithms

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Information

Published In

cover image ACM Conferences

RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

September 2018

600 pages

ISBN:9781450359016

DOI:10.1145/3240323

General Chairs:
Sole Pera
Boise State University
,
Michael Ekstrand
Boise State University
,
Program Chairs:
Xavier Amatriain
Curai, USA
,
John O'Donovan
University of California, Santa Barbara

Copyright © 2018 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 the author(s) 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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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

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Short-paper

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Ministry of Culture and Science of the German State of North Rhine-Westphalia

Conference

RecSys '18

Sponsor:

SIGCHI

RecSys '18: Twelfth ACM Conference on Recommender Systems

October 2, 2018

British Columbia, Vancouver, Canada

Acceptance Rates

RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;

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

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Cited By

Tachioka Y(2024)User Knowledge Prompt for Sequential RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691714(1142-1146)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691714
Feng KKoo XTan LBruckman AMcDonald DZhang A(2024)Mapping the Design Space of Teachable Social Media Feed ExperiencesProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642120(1-20)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642120
Muhammad RKasahara S(2024)Agent-based simulation of fake news dissemination: the role of trust assessment and big five personality traits on news spreadingSocial Network Analysis and Mining10.1007/s13278-024-01235-814:1Online publication date: 30-Mar-2024
https://doi.org/10.1007/s13278-024-01235-8
Ravikumar RJain SSarkar M(2024)AdaptiLearn: real-time personalized course recommendation system using whale optimized recurrent neural networkInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02301-2Online publication date: 5-Apr-2024
https://doi.org/10.1007/s13198-024-02301-2
Haruyama MHidaka K(2024)What influences users to provide explicit feedback? A case of food delivery recommendersUser Modeling and User-Adapted Interaction10.1007/s11257-023-09385-834:3(753-796)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11257-023-09385-8
Sun LZhou L(2024)Generative artificial intelligence attitude analysis of undergraduate students and their precise improvement strategies: A differential analysis of multifactorial influencesEducation and Information Technologies10.1007/s10639-024-13236-3Online publication date: 19-Dec-2024
https://doi.org/10.1007/s10639-024-13236-3
Xu YWang EYang YXiong H(2023)GS$^{2}$-RS: A Generative Approach for Alleviating Cold start and Filter bubbles in Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.3290140(1-14)Online publication date: 2023
https://doi.org/10.1109/TKDE.2023.3290140
Kieslich KKeller BStarke C(2022)Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligenceBig Data & Society10.1177/205395172210929569:1Online publication date: 10-May-2022
https://doi.org/10.1177/20539517221092956
Faisal MRizwan AIqbal KFasihuddin HBanjar ADaud A(2022)Prediction of Movie Quality via Adaptive Voting ClassifierIEEE Access10.1109/ACCESS.2022.319522810(81581-81596)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3195228
Jang YChoi SKim H(2022)Development and validation of an instrument to measure undergraduate students’ attitudes toward the ethics of artificial intelligence (AT-EAI) and analysis of its difference by gender and experience of AI educationEducation and Information Technologies10.1007/s10639-022-11086-527:8(11635-11667)Online publication date: 18-May-2022
https://doi.org/10.1007/s10639-022-11086-5
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