Fair and Transparent Recommender Systems for Advertisements

Dina Zilbershtein

doi:10.1145/3705328.3748755

Fair and Transparent Recommender Systems for Advertisements

Dina Zilbershtein^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

Recommender systems are central to digital platforms, powering content personalization, user engagement, and revenue generation. In advertising, they operate within a multi-stakeholder environment, bringing together viewers, advertisers, and platform providers with often competing objectives. While such systems enhance targeting precision, their opacity raises concerns around fairness, transparency, and trust. This research, conducted in collaboration with RTL Netherlands, focuses on building fair and transparent recommender systems for advertisements, with particular emphasis on Video-on-Demand (VoD) platforms. I investigate algorithmic interventions and explainability techniques aimed at aligning system behavior with stakeholders' expectations. By addressing tensions between stakeholders' objectives and challenges of the ad delivery process, this work contributes to the design of ethically responsible advertising systems that balance commercial goals with accountability and user trust.

Original language	English
Title of host publication	RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems
Publisher	Association for Computing Machinery, Inc
Pages	1473-1478
Number of pages	6
ISBN (Electronic)	9798400713644
DOIs	https://doi.org/10.1145/3705328.3748755
Publication status	Published - 7 Aug 2025
Event	19th ACM Conference on Recommender Systems - O2 universum Convention Center, Prague, Czech Republic Duration: 22 Sept 2025 → 26 Sept 2025 Conference number: 19 https://recsys.acm.org/recsys25/

Conference

Conference	19th ACM Conference on Recommender Systems
Abbreviated title	RecSys 2025
Country/Territory	Czech Republic
City	Prague
Period	22/09/25 → 26/09/25
Internet address	https://recsys.acm.org/recsys25/

Keywords

Advertisement Recommendations
Explainable Recommender Systems
Online Behavioural Advertising
Transparency
Video-on-Demand Platforms

Access to Document

10.1145/3705328.3748755

Cite this

@inproceedings{9496ee942f2d48e4a88815040700233a,

title = "Fair and Transparent Recommender Systems for Advertisements",

abstract = "Recommender systems are central to digital platforms, powering content personalization, user engagement, and revenue generation. In advertising, they operate within a multi-stakeholder environment, bringing together viewers, advertisers, and platform providers with often competing objectives. While such systems enhance targeting precision, their opacity raises concerns around fairness, transparency, and trust. This research, conducted in collaboration with RTL Netherlands, focuses on building fair and transparent recommender systems for advertisements, with particular emphasis on Video-on-Demand (VoD) platforms. I investigate algorithmic interventions and explainability techniques aimed at aligning system behavior with stakeholders' expectations. By addressing tensions between stakeholders' objectives and challenges of the ad delivery process, this work contributes to the design of ethically responsible advertising systems that balance commercial goals with accountability and user trust.",

keywords = "Advertisement Recommendations, Explainable Recommender Systems, Online Behavioural Advertising, Transparency, Video-on-Demand Platforms",

author = "Dina Zilbershtein",

note = "Funding Information: I would like to thank my supervisors, Francesco Barile, Daan Odijk, and Nava Tintarev, for their guidance, feedback, and support. This publication is part of the project ROBUST: Trustworthy AI-based Systems for Sustainable Growth with project number KICH3.LTP.20.006, which is (partly) financed by the Dutch Research Council (NWO), RTL, DPG, and the Dutch Ministry of Economic Affairs and Climate Policy (EZK) under the program LTP KIC 2020- 2025. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors. Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 19th ACM Conference on Recommender Systems, RecSys 2025 ; Conference date: 22-09-2025 Through 26-09-2025",

year = "2025",

month = aug,

day = "7",

doi = "10.1145/3705328.3748755",

language = "English",

pages = "1473--1478",

booktitle = "RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

url = "https://recsys.acm.org/recsys25/",

}

TY - GEN

T1 - Fair and Transparent Recommender Systems for Advertisements

AU - Zilbershtein, Dina

N1 - Conference code: 19

PY - 2025/8/7

Y1 - 2025/8/7

N2 - Recommender systems are central to digital platforms, powering content personalization, user engagement, and revenue generation. In advertising, they operate within a multi-stakeholder environment, bringing together viewers, advertisers, and platform providers with often competing objectives. While such systems enhance targeting precision, their opacity raises concerns around fairness, transparency, and trust. This research, conducted in collaboration with RTL Netherlands, focuses on building fair and transparent recommender systems for advertisements, with particular emphasis on Video-on-Demand (VoD) platforms. I investigate algorithmic interventions and explainability techniques aimed at aligning system behavior with stakeholders' expectations. By addressing tensions between stakeholders' objectives and challenges of the ad delivery process, this work contributes to the design of ethically responsible advertising systems that balance commercial goals with accountability and user trust.

AB - Recommender systems are central to digital platforms, powering content personalization, user engagement, and revenue generation. In advertising, they operate within a multi-stakeholder environment, bringing together viewers, advertisers, and platform providers with often competing objectives. While such systems enhance targeting precision, their opacity raises concerns around fairness, transparency, and trust. This research, conducted in collaboration with RTL Netherlands, focuses on building fair and transparent recommender systems for advertisements, with particular emphasis on Video-on-Demand (VoD) platforms. I investigate algorithmic interventions and explainability techniques aimed at aligning system behavior with stakeholders' expectations. By addressing tensions between stakeholders' objectives and challenges of the ad delivery process, this work contributes to the design of ethically responsible advertising systems that balance commercial goals with accountability and user trust.

KW - Advertisement Recommendations

KW - Explainable Recommender Systems

KW - Online Behavioural Advertising

KW - Transparency

KW - Video-on-Demand Platforms

U2 - 10.1145/3705328.3748755

DO - 10.1145/3705328.3748755

M3 - Conference article in proceeding

SP - 1473

EP - 1478

BT - RecSys2025 - Proceedings of the 19th ACM Conference on Recommender Systems

PB - Association for Computing Machinery, Inc

T2 - 19th ACM Conference on Recommender Systems

Y2 - 22 September 2025 through 26 September 2025

ER -

Fair and Transparent Recommender Systems for Advertisements

Abstract

Conference

Keywords

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Cite this