The Secret Algorithms That Control Your Love Life - Land of the Giants Recap

Podcast: Land of the Giants

Published: 2023-01-25

Duration: 30 min

Guests: Benjamin Berman, Jonathan Bedine, Amrnath Tombre, Kathy O'Neill, Fan Yun Zhang

Summary

Dating apps use secretive algorithms to match users, often leading to dissatisfaction due to their opaque methods and focus on keeping users engaged rather than finding perfect matches.

What Happened

Lakshmi Rangarajan experiments with 'Monster Match,' a simulated dating game, to understand real dating app algorithms. The game's profiles and swiping mechanisms mimic actual dating apps, revealing the influence of algorithms on user recommendations. Benjamin Berman, who developed Monster Match, was inspired by a friend's negative experiences with dating apps, highlighting the role of algorithms in these outcomes.

The episode delves into how dating apps like Tinder and Bumble use algorithms to match users, often without disclosing their inner workings. Jonathan Bedine, former Tinder executive, explains how the app's algorithm previously ranked users based on attractiveness, though this practice has since evolved. Bumble's algorithm remains opaque, while Hinge's uses a Nobel Prize-winning matching formula.

Amrnath Tombre, CEO of Match Group Americas, describes how early dating sites used simplistic algorithms, improving over time to focus on user behavior rather than stated preferences. The episode touches on how these algorithms have evolved, with companies like eHarmony and Match.com employing experts to build trust in their systems.

The episode features insights from Kathy O'Neill, a mathematician and data scientist, who critiques the trust placed in dating app algorithms. She argues that these systems use predictive algorithms based on historical data, which may not guarantee successful matches. Users often try to game these algorithms with strategies, but success is not assured.

Sarah Sadaroff and Jeremy, two dating app users, share their experiences attempting to manipulate app algorithms. Sadaroff found that saying yes to more matches did not improve her dating prospects, while Jeremy's generic profile attracted more matches but not better quality ones. Both illustrate the complexity and unpredictability of dating app algorithms.

Fan Yun Zhang, a professor at Columbia Business School, discusses her work with a dating app to improve match rates by 30%. Her insights reveal how apps use user data and algorithmic adjustments to enhance matching while keeping users engaged on the platform. This strategy reflects a balance between user satisfaction and company interests.

The episode concludes by questioning the trust placed in these algorithms and highlighting the potential for missed connections due to algorithmic biases. It discusses the broader implications of relying on technology for dating, suggesting that algorithms can favor some users over others, impacting dating success.

Key Insights