Fairness in Recommendation Systems: Consumer and Provider Perspectives

Recommendation systems are pervasive in daily life, influencing entertainment, news, shopping, and more.

Although they aim to serve users, several concerns arise: they can shape user preferences, and biased decisions affect both consumers and suppliers.

Bias originates from limited resources, leading to three main issues: limited user attention, limited recommendation slots, and data bias amplified by feedback loops.

These biases can erode trust and hinder long‑term sustainability, manifesting as the Matthew effect, filter bubbles, and market imbalance.

Fairness is commonly defined in two ways: Equality (equal opportunity for all) and Equity (adjusted support to achieve comparable outcomes), illustrated with gender and disability examples.

From the consumer side, fairness requires equal treatment across sensitive groups, ensuring recommendation outcomes do not differ significantly.

From the provider side, fairness calls for supporting smaller suppliers to prevent monopolies and ensure balanced exposure.

To quantify consumer‑side fairness, the Counterfactual Group Fairness (CGF) metric is introduced, with a relaxed variant.

Using causal analysis, user embeddings are decomposed into sensitive‑related and insensitive components via instrumental variable regression, allowing the model to learn fairer representations.

Provider‑side fairness is measured by Proportional Fairness (PF) and Max‑Min Fairness (MMF); the latter is preferred to uplift the weakest suppliers.

This leads to an online resource allocation problem where both consumer utility and provider fairness must be optimized, solved in the dual space with efficient CPU/GPU inference.

In summary, a multi‑role recommendation system should enforce equality for users (measured by CGF) and equity for suppliers (measured by MMF), employing causal embedding techniques and fairness‑aware optimization to achieve a sustainable ecosystem.