Cornell researchers have developed a fairer system for search recommendations—from hotels to jobs to videos—so a few top hits don't get all the exposure.

The new ranking system still provides relevant options, but divides user attention more equitably across search results. It can be applied to online markets such as travel sites, hiring platforms and news aggregators.

Yuta Saito, a doctoral student in the field of computer science and Thorsten Joachims, professor of computer science and information science in the Cornell Ann S. Bowers College of Computing and Information Science, described their new system in "Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking," published in the Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

"In recommender systems and search engines, whoever gets ranked high draws a lot of benefit from that," Joachims said. "The user's attention is a limited resource and we need to distribute it fairly among the items."

Conventional recommender systems attempt to rank items purely according to what users want to see, but many items receive unfairly low spots in the order. Items with similar merit can end up far apart in the rankings, and for some items, the odds of being discovered on a platform are worse than random chance.

To correct this issue, Saito developed an improved ranking system based on ideas borrowed from economics. He applied principles of "fair division"—how to allocate a limited resource, such as food, fairly among members of a group.

Saito and Joachims demonstrated the feasibility of the ranking system using synthetic and real-world data. They found it offers viable search results for the user, while fulfilling three fair division criteria: Every item's benefit from being ranked on the platform is better than being discovered at random; no item's impact, such as revenue, can easily be improved; and no item would gain an advantage by switching how it is ranked compared to other items in a series of searches.

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