A Reproducibility Study of Product-side Fairness in Bundle Recommendationcs.AI updates on arXiv.orgon July 22, 2025 at 4:00 am arXiv:2507.14352v1 Announce Type: cross
Abstract: Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended results. While this problem has been widely studied in traditional recommendation settings, its implications for bundle recommendation (BR) remain largely unexplored. This emerging task introduces additional complexity: recommendations are generated at the bundle level, yet user satisfaction and product (or supplier) exposure depend on both the bundle and the individual items it contains. Existing fairness frameworks and metrics designed for traditional recommender systems may not directly translate to this multi-layered setting. In this paper, we conduct a comprehensive reproducibility study of product-side fairness in BR across three real-world datasets using four state-of-the-art BR methods. We analyze exposure disparities at both the bundle and item levels using multiple fairness metrics, uncovering important patterns. Our results show that exposure patterns differ notably between bundles and items, revealing the need for fairness interventions that go beyond bundle-level assumptions. We also find that fairness assessments vary considerably depending on the metric used, reinforcing the need for multi-faceted evaluation. Furthermore, user behavior plays a critical role: when users interact more frequently with bundles than with individual items, BR systems tend to yield fairer exposure distributions across both levels. Overall, our findings offer actionable insights for building fairer bundle recommender systems and establish a vital foundation for future research in this emerging domain.
arXiv:2507.14352v1 Announce Type: cross
Abstract: Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended results. While this problem has been widely studied in traditional recommendation settings, its implications for bundle recommendation (BR) remain largely unexplored. This emerging task introduces additional complexity: recommendations are generated at the bundle level, yet user satisfaction and product (or supplier) exposure depend on both the bundle and the individual items it contains. Existing fairness frameworks and metrics designed for traditional recommender systems may not directly translate to this multi-layered setting. In this paper, we conduct a comprehensive reproducibility study of product-side fairness in BR across three real-world datasets using four state-of-the-art BR methods. We analyze exposure disparities at both the bundle and item levels using multiple fairness metrics, uncovering important patterns. Our results show that exposure patterns differ notably between bundles and items, revealing the need for fairness interventions that go beyond bundle-level assumptions. We also find that fairness assessments vary considerably depending on the metric used, reinforcing the need for multi-faceted evaluation. Furthermore, user behavior plays a critical role: when users interact more frequently with bundles than with individual items, BR systems tend to yield fairer exposure distributions across both levels. Overall, our findings offer actionable insights for building fairer bundle recommender systems and establish a vital foundation for future research in this emerging domain. Read More
Authored by Derrick Jackson & Co-Author Lisa Yu CompTIA A+ Certification: Your IT Career Starts Here March 19, 1993. Remember that date. I am sure half of the people reading this might not have even been born yet! That’s when the first person earned a CompTIA A+ certification. Back then, computer repair was the Wild […]
Authored by Derrick Jackson & Co-Author Lisa Yu | Last updated 09/21/2025 Pressed For Time? Review or Download our 2-3 min Quick Slides or the 5-7 min Article Insights to gain knowledge with the time you have! Security+ Certification Overview Your Foundation for Cybersecurity Success in a Transformed Market The cybersecurity job landscape isn’t what it was three years […]
Author: Derrick D. JacksonTitle: Founder & Senior Director of Cloud Security Architecture & RiskCredentials: CISSP, CRISC, CSSPLast updated June 2nd, 2025 Article 2 in the Executive AI Leadership Series Building Your AI Governance Framework: From Strategy to Implementation The Reality Check You’ve made the case. The board understands AI governance isn’t optional. But what matters is […]
Authored by Derrick Jackson & Co-Author Lisa Yu : updated 10/07/2025 CISSP Certification Overview CISSP Certification Guide: Requirements, Cost, Salary & How Hard Is It? (2025) Most cybersecurity certifications validate technical skills. CISSP certification does something different. It validates whether you can lead. Whether you understand the business impact of security decisions. Whether you’ve accumulated enough real-world experience […]
A comparative analysis showing how different approaches to AI frameworks serve distinct organizational needs while maintaining industry alignment. In this article you will learn: The Reality Check As organizations race to implement AI systems, they’re juggling security threats, regulatory demands, and business pressures all at once. It’s messy, and frankly, it’s not surprising. Around 70% […]
Author: Derrick D. JacksonTitle: Founder & Senior Director of Cloud Security Architecture & RiskCredentials: CISSP, CRISC, CCSPLast updated: May 30th, 2025 AI Use Case Trackers You know what’s funny about AI governance? Everyone talks about it, but most organizations are flying blind. They’ve got AI systems scattered across departments, no one knows who owns what, and when […]
The Culprit Ransomware is predicted to hit someone every 2 seconds by 2031. When it happens, you’re looking at 3+ weeks of downtime and millions in recovery costs. Here’s what actually works to prevent it, based on environments where people have successfully fought these attacks. Windows Environment Get endpoint protection that isn’t garbage Most antivirus is […]
The Need for Explainable AI Between 2013 and 2019, the Dutch tax authority’s algorithm flagged 26,000 families as potential fraudsters. The system worked exactly as programmed, spotting patterns in childcare benefit claims. But when investigators finally understood what the algorithm was doing, they discovered it was using nationality as a hidden factor. The result: thousands […]
AI Explainability Your smartphone can recognize your face in milliseconds. But ask it why it thinks that’s you instead of your twin, and you’ll get digital silence. Or a Confabulation (le’t s keep it real). This isn’t a small problem. We’re building AI systems that make medical diagnoses, approve loans, and control autonomous vehicles. Yet […]