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Data Quality Matters in Recommender Systems

Authors:

Amir AmihoodAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 257 - 260

https://doi.org/10.1145/2792838.2799670

Published: 16 September 2015 Publication History

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Abstract

Although data quality has been recognized as an important factor in the broad information systems research, it has received little attention in recommender systems. Data quality matters are typically addressed in recommenders by ad-hoc cleansing methods, which prune noisy or unreliable records from the data. However, the setting of the cleansing parameters is often done arbitrarily, without thorough consideration of the data characteristics. In this work, we turn to two central data quality problems in recommender systems: sparsity and redundancy. We devise models for setting data-dependent thresholds and sampling levels, and evaluate these using a collection of public and proprietary datasets. We observe that the models accurately predict data cleansing parameters, while having minor effect on the accuracy of the generated recommendations.

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Cited By

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Adamopoulos PTodri V(2024)Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation AppProduction and Operations Management10.1177/10591478241259422Online publication date: 25-Jul-2024
https://doi.org/10.1177/10591478241259422
Wegmeth L(2023)Improving Recommender Systems Through the Automation of Design DecisionsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608877(1332-1338)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608877
Bibas KSar Shalom OJannach D(2023)Semi-supervised Adversarial Learning for Complementary Item RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583462(1804-1812)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583462
Show More Cited By

Index Terms

Data Quality Matters in Recommender Systems
1. Information systems
  1. World Wide Web
    1. Web applications
    2. Web services

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Information & Contributors

Information

Published In

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, Italy

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

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RecSys '15

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SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

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RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

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

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Cited By

View all

Adamopoulos PTodri V(2024)Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation AppProduction and Operations Management10.1177/10591478241259422Online publication date: 25-Jul-2024
https://doi.org/10.1177/10591478241259422
Wegmeth L(2023)Improving Recommender Systems Through the Automation of Design DecisionsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608877(1332-1338)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608877
Bibas KSar Shalom OJannach D(2023)Semi-supervised Adversarial Learning for Complementary Item RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583462(1804-1812)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583462
Reinhard PLi MDickhaut EPeters CLeimeister J(2023)Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-Based Textual Data Quality EnrichmentDesign Science Research for a New Society: Society 5.010.1007/978-3-031-32808-4_18(279-293)Online publication date: 19-May-2023
https://doi.org/10.1007/978-3-031-32808-4_18
Avny Brosh TLivne ASar Shalom OShapira BLast M(2022)BRUCE: Bundle Recommendation Using Contextualized item EmbeddingsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546754(237-245)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546754
Bibas KSar Shalom OJannach DAl Hasan MXiong L(2022)Collaborative Image UnderstandingProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557260(77-87)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557260
Eden SLivne ASar Shalom OShapira BJannach DBellogin ABoratto LSantos OArdissono LKnijnenburg B(2022)Investigating the Value of Subtitles for Improved Movie RecommendationsProceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3503252.3531291(99-109)Online publication date: 4-Jul-2022
https://dl.acm.org/doi/10.1145/3503252.3531291
Bernardis CDacrema MMaurera FQuadrana MScriminaci MCremonesi P(2022)From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video DomainIEEE Access10.1109/ACCESS.2022.314843410(14779-14796)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3148434
Cohen RSar Shalom OJannach DAmir ALewin-Eytan LCarmel DYom-Tov EAgichtein EGabrilovich E(2021)A Black-Box Attack Model for Visually-Aware Recommender SystemsProceedings of the 14th ACM International Conference on Web Search and Data Mining10.1145/3437963.3441757(94-102)Online publication date: 8-Mar-2021
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Heinrich BHopf MLohninger DSchiller ASzubartowicz M(2020)Something’s Missing? A Procedure for Extending Item Content Data Sets in the Context of Recommender SystemsInformation Systems Frontiers10.1007/s10796-020-10071-y24:1(267-286)Online publication date: 22-Oct-2020
https://doi.org/10.1007/s10796-020-10071-y
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