Алгоритмический подбор партнеров и анализ привлекательности

Уровень 0 · материалов: 4

В кластер входят документы о применении математических методов, анализа данных и нейросетей для оценки привлекательности или поиска романтического партнера.

Общие признаки: математические модели в поиске партнера, использование данных и алгоритмов, анализ внешней привлекательности, сопоставление совместимости

Группа выше: Прикладные задачи машинного обучения

Материалы

• #29768 — Mathematician Uses Data Mining to Find Perfect Partner — Представитель этого кластера

  Математик взломал сайт знакомств и нашёл девушку мечты — alizar

  Смысл: The main idea is that applying scientific methods, specifically data mining and clustering, can optimize the search for a romantic partner by filtering for compatibility and visibility, although human chemistry still requires real-world trial and error.

  A mathematician used supercomputers and Python scripts to mine OkCupid's data, clustering potential partners to find a 91% compatible match and eventual fiancé.

• #19254 — Hacking Dating Algorithms for Love

  Взлом по любви. Как математик взломал алгоритм сайта знакомств и нашел идеальную девушку — Holmogorov

  Смысл: The main idea is that while mathematical models and data mining can significantly increase the efficiency of finding a compatible partner by filtering for shared interests, the actual success of a relationship depends on human chemistry and real-life interaction which cannot be fully algorithmized.

  A mathematician used Python scripts and clustering algorithms to reverse-engineer OkCupid's matching system, eventually finding his future wife through a combination of data science and trial-and-error dating.

• #14127 — Using Machine Learning to Automate Tinder Swiping

  Как клеить по 13 девушек в час, используя машинное обучение и Tinder — ruvds

  Смысл: The text serves as a technical case study demonstrating how computer vision and neural networks can be used to automate subjective decision-making (attractiveness) on a dating platform to overcome statistical disadvantages.

  A developer creates a convolutional neural network to automate Tinder swiping based on personal taste, achieving 13 matches per hour before advising readers to actually date in real life.

• #34155 — Digitalization of Beauty through Algorithmic Facial Adjustment

  Оцифровать красоту — meteor

  Смысл: The main idea is that human beauty can be mathematically modeled and digitally manipulated using algorithms based on statistical data of perceived attractiveness.

  Researchers at Tel Aviv University developed a 'Beauty Machine' algorithm that modifies facial proportions in photos to meet mathematical standards of beauty.