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from the department-of-unwanted-hyperfocus dept.
Researchers at the Cornell and the Technische Univerität Berlin and Cornell have studied the problem that more popular items get priority in search results, creating a positive feedback loop that unfairly deprecates other, equally valuable items.
Rankings are the primary interface through which many online platforms match users to items (e.g. news, products, music, video). In these two-sided markets, not only the users draw utility from the rankings, but the rankings also determine the utility (e.g. exposure, revenue) for the item providers (e.g. publishers, sellers, artists, studios). It has already been noted that myopically optimizing utility to the users – as done by virtually all learning-to-rank algorithms – can be unfair to the item providers. We, therefore, present a learning-to-rank approach for explicitly enforcing merit-based fairness guarantees to groups of items (e.g. articles by the same publisher, tracks by the same artist). In particular, we propose a learning algorithm that ensures notions of amortized group fairness, while simultaneously learning the ranking function from implicit feedback data. The algorithm takes the form of a controller that integrates unbiased estimators for both fairness and utility, dynamically adapting both as more data becomes available. In addition to its rigorous theoretical foundation and convergence guarantees, we find empirically that the algorithm is highly practical and robust.
Journal Reference:
Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020. Controlling Fairness and Bias in Dynamic Learning-to-Rank. In Proceedings
of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20), July 25–30, 2020, Virtual Event, China.
ACM, NewYork, NY, USA. DOI: https://doi.org/10.1145/3397271.3401100
Maybe this, if deployed widely, can help reduce the tendencies for discourse to develop isolated silos.
(Score: 2) by The Mighty Buzzard on Monday August 24 2020, @05:06PM (4 children)
Yup, there should be a way to say "no, that appeared useful but was not."
My rights don't end where your fear begins.
(Score: 0) by Anonymous Coward on Monday August 24 2020, @06:51PM (1 child)
This is why they want tracking embedded, so the “no” can be inferred by later actions. A users opinion on usefulness means nothing to marketeers.
(Score: 2) by acid andy on Monday August 24 2020, @07:28PM
That's why a good search engine would only be funded by old-fashioned banner ads and its ranking should have nothing whatsoever to do with marketing. Nah, I don't think anyone's going to do it anymore either.
I know Duckduckgo isn't supposed to track users but it uses results from some search engines that do so, I don't know.
If a cat has kittens, does a rat have rittens, a bat bittens and a mat mittens?
(Score: 1, Insightful) by Anonymous Coward on Tuesday August 25 2020, @02:00AM (1 child)
Definitely. It pisses me off seeing all those links to resources where I have to pay money or join the site in order to see the full result. Expertsexchange and quora are particularly egregious examples. Not to mention the various random linkfarms that just exist to capitalize on random accidental clicks. Getting those demoted down to the bottom of the list would be great for everybody else.
(Score: 0) by Anonymous Coward on Tuesday August 25 2020, @05:16AM
"Expert sex change", now there's a naming disaster with recipe for disappointment.