# Addressing Marketing Bias in Product Recommendations

arXiv: https://arxiv.org/abs/1912.01799 · HTML: https://mlsanity.com/paper/1912.01799/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 23 of 23)

- [Fairness in Graph Mining: A Survey](https://mlsanity.com/paper/2204.09888/) (2022)
- [A Comprehensive Survey on Trustworthy Recommender Systems](https://mlsanity.com/paper/2209.10117/) (2022)
- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Understanding Biases in ChatGPT-based Recommender Systems: Provider Fairness, Temporal Stability, and Recency](https://mlsanity.com/paper/2401.10545/) (2024)
- [Fairness in Recommendation: Foundations, Methods and Applications](https://mlsanity.com/paper/2205.13619/) (2022)
- [Ensuring User-side Fairness in Dynamic Recommender Systems](https://mlsanity.com/paper/2308.15651/) (2023)
- [Learning Robust Recommenders through Cross-Model Agreement](https://mlsanity.com/paper/2105.09605/) (2021)
- [LLM Whisperer: An Inconspicuous Attack to Bias LLM Responses](https://mlsanity.com/paper/2406.04755/) (2024)
- ["Global is Good, Local is Bad?": Understanding Brand Bias in LLMs](https://mlsanity.com/paper/2406.13997/) (2024)
- [A survey on fairness of large language models in e-commerce: progress, application, and challenge](https://mlsanity.com/paper/2405.13025/) (2024)
- [The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation](https://mlsanity.com/paper/2202.13307/) (2022)
- [Intersectional Two-sided Fairness in Recommendation](https://mlsanity.com/paper/2402.02816/) (2024)
- [FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data](https://mlsanity.com/paper/2212.06750/) (2022)
- [Causal Lifting and Link Prediction](https://mlsanity.com/paper/2302.01198/) (2023)
- [On the Generalizability and Predictability of Recommender Systems](https://mlsanity.com/paper/2206.11886/) (2022)
- [Unmasking Gender Bias in Recommendation Systems and Enhancing Category-Aware Fairness](https://mlsanity.com/paper/2502.17921/) (2025)
- [Stairway to Fairness: Connecting Group and Individual Fairness](https://mlsanity.com/paper/2508.21334/) (2025)
- [Consumer-side Fairness in Recommender Systems: A Systematic Survey of Methods and Evaluation](https://mlsanity.com/paper/2305.09330/) (2023)
- [DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model](https://mlsanity.com/paper/2410.02791/) (2024)
- [(Un)fair Exposure in Deep Face Rankings at a Distance](https://mlsanity.com/paper/2308.11732/) (2023)
- [Label Denoising through Cross-Model Agreement](https://mlsanity.com/paper/2308.13976/) (2023)
- [Fairness Evaluation for Uplift Modeling in the Absence of Ground Truth](https://mlsanity.com/paper/2403.12069/) (2024)
- [An Investigation of Linguistic Biases in LLM-Based Recommendations](https://mlsanity.com/paper/2604.25456/) (2026)

## Abstract (arXiv)

Modern collaborative filtering algorithms seek to provide personalized product recommendations by uncovering patterns in consumer-product interactions. However, these interactions can be biased by how the product is marketed, for example due to the selection of a particular human model in a product image. These correlations may result in the underrepresentation of particular niche markets in the interaction data; for example, a female user who would potentially like motorcycle products may be less likely to interact with them if they are promoted using stereotypically 'male' images. In this paper, we first investigate this correlation between users' interaction feedback and products' marketing images on two real-world e-commerce datasets. We further examine the response of several standard collaborative filtering algorithms to the distribution of consumer-product market segments in the input interaction data, revealing that marketing strategy can be a source of bias for modern recommender systems. In order to protect recommendation performance on underrepresented market segments, we develop a framework to address this potential marketing bias. Quantitative results demonstrate that the proposed approach significantly improves the recommendation fairness across different market segments, with a negligible loss (or better) recommendation accuracy.