  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.
