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Big Data в моде: как мы внедрили 1-to-1 персонализацию в каталоге и поиске

Привет, Хабр! Это Сергей Евстафьев и Дана Злочевская из команды ранжирования и поиска Lamoda Tech. Наша задача — помочь пользователю найти то, что ему нужно, и не потеряться в море доступных вариантов.

В каталоге Lamoda в наличии более полумиллиона модных товаров, однако 95% пользователей не просматривают больше первых 120 карточек. Поэтому в первую очередь важно показывать только самую релевантную подборку, для этого мы развиваем персональное ранжирование каталога. С его помощью каждый пользователь видит свою уникальную выдачу, которая собирается на основе его поведения, популярности товаров и других параметров.

Организовать такое ранжирование можно разными способами. Мы развивались поэтапно: в течение нескольких лет переходили от эвристик к внедрению ML, улучшая пайплайн ранжирования.

В этой статье поподробнее раскроем наш подход. Итак, нам предстоит обсудить:

Из каких этапов состоит персональное ранжирование каталога.
Как работает первый этап персонализации — отбор кандидатов.
Что из себя представляет второй этап — онлайн переранжирование топа выдачи.

История ранжирования в Lamoda

Ранжирование каталога — один из самых крупных и важных продуктов для Lamoda.

Оно пережило три этапа улучшений:

В начале все ранжирование было основано на статистике и эвристических правилах. Мы делили пользователей на сегменты, в каждом из которых заранее рассчитывали свое оптимальное ранжирование товаров в каталоге на базе популярности товаров.
В 2020 году внедрили ML в ранжирование для сегментов пользователей. Теперь вероятность добавления в корзину предсказывается ML-моделью для каждого товара и сегмента.
В 2023 году внедрили ML-персонализацию для каждого пользователя, которая переранжирует топ товаров в выдаче в момент запроса.

Исторический путь развития ранжирования в Lamoda

Про первые два этапа мы рассказывали в предыдущей статье. Сейчас же хотим поделиться тем, какой путь мы прошли в персонализации каталога для каждого, с какими трудностями столкнулись и как планируем развиваться дальше.

Двухэтапное персональное ранжирование

Итак, у нас есть ранжирование всего каталога для крупных сегментов пользователей и оно работает по следующей схеме:

Как определяются сегменты в сегментном ранжировании

Но как переходить к персонализации?

Основная сложность в том, что персонализация целого каталога под каждого пользователя — ресурсоемкая задача: на Lamoda больше 600 000 товаров в наличии и их количество растет вместе с ростом бизнеса. Поэтому мы начали думать, как ограничить список товаров, которые будут ранжироваться персонально.

Конечно, мы не первые, кто сталкивается с такой задачей. Зарекомендовавший себя подход в индустрии, который уже стал классическим для рекомендательных систем, состоит из двух этапов:

Отбор кандидатов для ранжирования из разных источников. На этом этапе мы формируем список из нескольких сотен товаров, которые являются самыми релевантными для пользователя.
Переранжирование. Чтобы ранжировать эти товары оптимальным образом, мы применяем тяжелые модели с полным набором фичей по товару и пользователю — и отдаем итоговую сортировку.

Взяв эту схему за основу, нам предстояло реализовать ее с использованием имеющихся у нас инструментов.

Первый этап — буст товаров-кандидатов с помощью ElasticSearch

ElasticSearch — это эффективное решение для запросов в большие поисковые индексы. Наш сервис каталога активно использует его как основной поисковый и ранжирующий движок, в нем же хранится информация по товарам и их атрибутам. Также в нем хранятся скоры релевантности товаров, рассчитанные для сегментов пользователей после первого этапа ранжирования.

Поэтому мы решили попробовать использовать этот инструмент, чтобы сразу учитывать персональные предпочтения пользователей. Таким образом мы можем объединить сегментное ранжирование и отбор кандидатов в один запрос в Elastic.

Для реализации этой идеи мы воспользовались функционалом function score в ElasticSearch, который позволяет добавить функцию скора/релевантности для товара, основываясь на его атрибутах и запросе пользователя. Эта функция может быть сложной, учитывающей разные атрибуты товара с разными весами. Вот как можно встроить эту функцию в запрос, указав в качестве весов релевантность конкретного атрибута пользователя товару:

{
  "query": {
    "bool": {
      "filter": [
        ... // Простые фильтры в каталоге
      ],
      "must": [
        {
          "function_score": {
            "functions": [
              // Персональные предпочтения
              {
                "filter": { "term": { "brand": "Zarina" } },
                "weight": user_weight_brand_Zarina
              },
              {
                "filter": { "term": { "color": "black" } },
                "weight": user_weight_color_black
              }
            ],
            "score_mode": "sum",
            "boost_mode": "sum",
            "query": {
              "function_score": {
                "boost_mode": "replace",
                "functions": [
                  {
                    "script_score": { ... } // Популярность + релевантность из сегментного ранжирования
                  }
                ],
                "query": { ... } // Фильтрующий поисковый запрос
              }
            }
          }
        }
      ]
    }
  }
}

Идея в том, чтобы добавить к скору релевантности из сегментного ранжирования скор, отражающий предпочтения пользователя к атрибутам товара. Для этого можно использовать в качестве значений функции скора основные характеристики товаров, а в качестве весов — отношение пользователя к этим атрибутам с учетом их важности. Таким образом мы будем добавлять вес (поднимать выше в выдаче) товары, атрибуты которых «нравятся» пользователю.

Рассчитываем пользовательские предпочтения

Для товаров в разных категориях мы знаем более 30 атрибутов: к какому бренду принадлежит товар, его цвет, размер, наличие рисунка, и так далее. Также мы знаем, с какими товарами взаимодействовал каждый пользователь: нажимал на карточку товара, добавлял в корзину или избранное, покупал. У каждого такого взаимодействия будет свой вес. Чуть больший коэффициент — у заказов, поменьше — у добавлений в корзину и в избранное.

С помощью этих данных мы оцениваем предпочтения пользователя по атрибутам. Например, как часто он добавлял в корзину бренд Lime, выкупал вещи размера XS или сколько раз добавлял в избранное вещи розового цвета. На выходе мы получаем вектора предпочтений пользователя к значениям атрибутов (например, к разным брендам, цветам и тд), рассчитанные для разных категорий.

Влияние фасетов

У себя эти признаки пользователя мы называем фасетными весами или просто фасетами.

Рассчитываем веса атрибутов и формируем финальный score

Хорошо, для пользователей мы рассчитали предпочтения. Как теперь модифицировать ранжирующий score ElasticSearch, чтобы учесть предпочтения органически вместе с релевантностью из первого этапа ранжирования?

Для этого необходимо понять, с какими весами учитывать склонность пользователя к каждому атрибуту, потому что они не равнозначны. Ведь любовь пользователя к бренду может оказаться важнее предпочтений по длине рукавов.

Чтобы понять с каким весом учитывать разные атрибуты - обучим логистическую регрессию предсказывать вероятность добавления пользователем товара в корзину на фичах его склонности к атрибутам товара. А чтобы учесть скор-релевантность из первого этапа ранжирования, также добавим его как фичу. Модели будем учить отдельно для одежды, обуви, аксессуаров товаров для дома и косметики, чтобы, к примеру, вес бренда был разный у бутс abibas и футболки abibas.

Обучив таким образом модель, мы получим коэффициенты при атрибутах, которые будут отражать их важность. Дальше мы разделим каждый коэффициент на модуль коэффициента при фиче «скора-релевантности» из первого этапа ранжирования (sfPos на картинке ниже) и получим финальные коэффициенты. Эта нормировка нужна, чтобы органически учесть коэффициенты при атрибутах, вместе со скором релевантности из первого этапа ранжирования в момент работы запроса. При этом отрицательные коэффициенты мы не учитываем, просто обнуляя их, чтобы давать товарам только положительный дополнительный буст.

Нормировка коэффициентов логистической регрессии

Таким образом мы получаем коэффициенты, на которые будем домножать персональные фасетные веса в function score, чтобы дать дополнительный буст товарам, релевантным пользователю.

Давайте посмотрим, как теперь будет выглядеть function_score в запросе к ElasticSearch с весами, полученными в примере выше. Для пользователя, отправившего запрос, мы перемножаем его предпочтения к брендам, размерам и цветам с нормированными коэффициентами из модели логистической регрессии (w_brand = 0.313, w_size = 0.137, w_color = 0.175).

{
  "function_score": {
    "boost_mode": "sum",
    "score_mode": "sum",
    "functions": [
      { 
        "filter": { "term": { "brand": "Zarina" } }, 
        "weight": 0.313 * 3 
      },
      { 
        "filter": { "term": { "brand": "Mango" } }, 
        "weight": 0.313 * 2.5 
      },
      { 
        "filter": { "term": { "brand": "Abibas" } }, 
        "weight": 0.313 * 0.1 
      },
      { 
        "filter": { "term": { "size": "L" } }, 
        "weight": 0.137 * 1.5 
      },
      { 
        "filter": { "term": { "size": "M" } }, 
        "weight": 0.137 * 3 
      },
      { 
        "filter": { "term": { "color": "black" } }, 
        "weight": 0.175 * 5 
      },
      { 
        "filter": { "term": { "color": "pink" } }, 
        "weight": 0.175 * 0.1 
      }
    ]
  }
}

Предпочтения пользователя по атрибутам товаров рассчитываются ежедневно. При «холодном старте» будет использоваться только сегментное ранжирование.

Под капотом эластик просуммирует полученные значения по всем атрибутам со скором от сегментного ранжирования и таким образом мы получим персональный буст товаров.

Тестируем результат

После добавления буста персональных кандидатов, выдачи у пользователей кардинальным образом изменились. Посмотрим на примере девушки, которая предпочитает вещи определенных брендов и цветов.

На первой картинке — результат выдачи сегментного ранжирования, на второй — сегментного ранжирования с фасетными весами. По фасетным весам и выдаче видно, что девушка предпочитает одежду черного цвета, длинные юбки и платья, а также бренд Zarina. Без фасетов большинство из этих вещей не попало бы даже в топ-500.

Результат нам очень понравился! Мы сразу покатили это в А/В-тестирование.

Однако желаемых результатов мы не добились: поведенческие кликовые метрики выросли, но целевую метрику нам прокрасить не удалось — роста покупок и заказов не произошло. Мы немного расстроились, но главные ожидания были от второго этапа, тест которого был еще впереди.

Второй этап — переранжирование

Первой идеей реализации модели второго уровня для переранжирования было подключение LTR-плагина в ElasticSearch: в нем можно использовать в том числе градиентный бустинг. Так все наше ранжирование жило бы внутри эластика: сначала сегментное ранжирование и буст персональных кандидатов из первого этапа, а затем переранжирование небольшого топа. Однако, у этого решения есть два минуса. Во-первых, для его грамотного использования необходимо будет работать с нетипичным для нас Java стеком. А во-вторых, мы всегда будем ограничены реализованным функционалом плагина.

Поэтому мы решили пойти по пути создания отдельного сервиса на Golang — быстром, компилируемом языке, в котором у нас есть опыт и ресурс разработки.

Его задача — запускать регулярно переобучаемую модель на фичах, собранных из заданных баз данных, в тот момент, когда пользователь запрашивает страницу каталога. И поскольку сервис независим, мы никак не ограничены в способах формирования фичей и запуска модели.

Данные, признаки, обучение модели

Для обучения модели мы используем датасет из пользовательских сессий, соответствующих просмотру товаров в конкретной категории каталога или поиске. К каждому просмотру мы атрибуцируем событие добавления пользователем товара в корзину, таким образом, в полученной группе взаимодействий негативными примерами являются просмотры товара без целевого события, а позитивными — с ним.

Чтобы качественно обучить модель для всех видов устройств с учетом неравномерного трафика, мы сэмплируем фиксированное количество групп из одного дня логов по каждой из платформ (мобильный сайт, desktop, Android, iOS).

В качестве фичей мы используем товарные признаки из модели сегментного ранжирования, а также добавляем фасетные признаки пользователя, счетчики добавления в корзину, просмотров и заказов по временным окнам.

На наших данных наилучшее качество показала модель CatBoost c лоссом YetiRank. Интересно, что по экспериментам, негативное семплирование только 20% показов без целевого действия никак не ухудшает качество модели, зато позволяет использовать больше дней логов, что дает лучшее качество. Благодаря этому, сейчас мы ежедневно обучаемся на 17 последних днях.

Основной нашей метрикой для оффлайн-оценки является NDCG@60, но мы также мониторим ряд качественных метрик – разнообразие выдачи, цена топа и т.д.

Инфраструктура полученного решения выглядит следующим образом. Регулярные оффлайн-части пайплайна – сбор фичей, сбор трейн-датасета и обучение моделей происходят на нашем hadoop-кластере ежедневно с использованием Spark. После этого модель загружается в S3, мета информация по ней в etcd, а обновленные фичи по пользователю и товарам в Aerospike – key-value базу с быстрым доступом в онлайне. Из этих источников go-сервис подгружает саму модель и собирает фичи для конкретного запроса.

Пайплайн работы модели

Давайте теперь резюмируем и пробежимся по всему пайплайну персонального ранжирования на проде. Приходит пользователь, что-то выбирает в каталоге или ищет в поиске, в это время на бэкенде формируется запрос в Service Catalog. Этот сервис обогащает запрос контекстом пользователя и отправляет его в Elastic, задача которого — отфильтровать подходящие запросу товары и сделать фасетное ранжирование.

После этого топ 300 кандидатов по итоговому скору Elastic отправляется в сервис переранжирования Service Ranking, где для них и пользователя подтягиваются все необходимые фичи для применения модели. И далее, наконец, происходит инференс CatBoost-модели, которая возвращает итоговое ранжирование в Service Catalog, а он в свою очередь отдает его пользователю.

И немного технических деталей: для сервиса Service Ranking мы стараемся держать SLA 250 мс, 50 перцентиль времени ответа составляет 50мс. Время ответа критически важно для качества вашего пайплайна в онлайне, поскольку оно напрямую влияет на бизнес-метрики.

Важность фичей

SHAP importance

Давайте теперь покопаемся в обученной модели, изучим полученную важность фичей. В топ выбились предпочтения пользователей к брендам, размерам и цветам, далее признаки конверсионности товаров, их популярности, цена, рейтинг и т. д. относительно равномерно распределяются по топу.

История запусков

Первым экспериментом был А/В-тест по добавлению сервиса переранжирования для 150 кандидатов. Результат этого теста был очень успешным: прокрасилась вся воронка. То есть пользователи начали чаще кликать, чаще добавлять в корзину, чаще делать покупки. Мы сделали так, что результат понравился и пользователям, и бизнесу. По метрикам мы получили +2% NMV и +1.7% к конверсии в выкуп.

Следующим успешным запуском стал А/В-тест, в котором мы расширили работу сервиса переранжирования на поисковые запросы, а также увеличили число кандидатов до 300. Также мы начали ежедневно переобучать модель. Суммарно эти улучшения дали нам дополнительно +1.3% NMV.

Результаты и выводы

Давайте теперь вернемся к той самой девушке, любительнице черного, длинных юбок и Zarina. Посмотрим, как изменилась ее выдача, когда мы добавили модель второго уровня:

Стиль выдачи сохранился и даже больше ушел в черное. Бренда Zarina стало чуть меньше, свободных штанов чуть больше. Визуально это не так заметно, как при применении только фасетов на первом этапе. Но бизнес-метрики при этом возросли существенно!

Подведем итог:

Персонализация ранжирования — работает и увеличивает
как поведенческие, так и бизнес-метрики.
Отбор кандидатов через Elasticsearch дает достаточную гибкость и фильтрацию «из коробки».
Отдельный сервис на Golang с CatBoost моделью второго уровня — рекомендуем! Это позволяет гибко управлять кандидатами, которые идут на вход модели, без проблем улучшать и тестировать модель в офлайне и делать быстрый инференс в онлайне.

Что дальше?

Конечно, у нас много планов по дальнейшему развитию, вот небольшой шортлист:

Научиться добавлять кандидатов в топ из других источников/моделей

На данный момент единственным источником кандидатов для модели переранжирования является сегментное ранжирование с фасетной персонализацией. Мы хотим расширить количество источников кандидатов для модели второго уровня, чтобы обеспечить большее разнообразие и релевантность товаров в топе.

Увеличить количество кандидатов

Сейчас на втором этапе мы можем переранжировать только 300 кандидатов. Безусловно, хочется больше, поэтому предстоит масштабировать текущий сервис.

Отказаться от временного отставания

В текущей реаализации персонализация происходит с отставанием в день. Мы пока что не умеем в онлайне подхватывать действия пользователей, но двигаемся в эту сторону, добавляя в модель горячие фичи по пользователю.

Избавиться от сдвига в данных между обучением и применением модели

Сейчас фичи для обучения модели собираются на исторических данных кликстрима, а применяется модель на фичах, которые ежедневно заливаются в БД или берутся из прод-систем (например – доступность стока). Поэтому мы работаем над избавлением от сдвига между фичами на инференсе и при обучении, который так или иначе происходит. Мы хотим быть уверенными, чтобы фичи, которые мы используем для построения train dataset, были прям в точности те же самые, что и в момент применения модели. Для этого планируем логировать фичи в момент инференса модели и переобучать модель именно на них.

Как видите, впереди еще много интересных задач, обязательно будем делиться результатами здесь и в Телеграм-канале Lamoda Tech. Если вам понравилось то, что мы делаем, и вы хотите стать частью новых решений — сейчас есть возможность присоединиться к нашей команде ранжирования и поиска. Приходите в личку: Сергей, Дана.

Спасибо что прочитали, stay tuned!

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3 сент. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

The "Shibarium for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH. The Plot Thickens. Someone posted the actual transactions!!!! https://bscscan.com/tx/0xa846ea0367c89c3f0bbfcc221cceea4c90d8f56ead2eb479d4cee41c75e02c97 It seems the article is true!!!! And it's also FUD. Let me explain. Check this link: https://bscscan.com/token/0x5a752c9fe3520522ea88f37a41c3ddd97c022c2f So there really is a "Shibarium" token. And somebody did a rug pull with it. CONFIRMED. But the "Shibarium" token for this confirmed rug pull is a BEP-20 project not related at all to Shibarium, SHIB, BONE or LEASH.
24.06.24 04:31 tashandiarisha

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26.06.24 18:46 Jacobethannn098

LEGAL RECOUP FOR CRYPTO THEFT BY ADRIAN LAMO HACKER
26.06.24 18:46 Jacobethannn098

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04.07.24 04:49 ZionNaomi

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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02.09.24 20:24 [email protected]

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06.09.24 01:35 Celinagarcia

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06.09.24 01:44 Celinagarcia

HOW TO RECOVER MONEY LOST IN BITCOIN/USDT TRADING OR TO CRYPTO INVESTMENT !! Hi all, friends and families. I am writing From Alberton Canada. Last year I tried to invest in cryptocurrency trading in 2023, but lost a significant amount of money to scammers. I was cheated of my money, but thank God, I was referred to Hack Recovery Wizard they are among the best bitcoin recovery specialists on the planet. they helped me get every penny I lost to the scammers back to me with their forensic techniques. and I would like to take this opportunity to advise everyone to avoid making cryptocurrency investments online. If you have already lost money on forex, cryptocurrency or Ponzi schemes, please contact [email protected] or WhatsApp: +1 (757) 237–1724 at once they can help you get back the crypto you lost to scammers. BEST WISHES. Celina Garcia.
16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
18.10.24 09:34 freidatollerud

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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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17.11.24 09:31 Vivianlocke223

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My entire existence fell apart when a malevolent hacker recently gained access to my online accounts. I felt violated and extremely uneasy after discovering that the digital platforms I depended on for communication, employment, and finances had been compromised. Regaining control and restoring my digital security was an overwhelming task in the immediate aftermath. To help me navigate the difficult process of recovering my accounts and getting my peace of mind back, TRUST GEEKS HACK EXPERT came into my life as a ray of hope. They immediately put their highly skilled professionals to work, thoroughly examining the vulnerability and methodically preventing unwanted access. They guided me through each stage soothingly, explaining what was occurring and why, so I never felt lost or alone. They communicated with service providers to restore my legitimate access while skillfully navigating the complex labyrinth of account recovery procedures. My digital footprint was cleaned and strengthened against future attacks thanks to their equally amazing ability to remove any remaining evidence of the hacker's presence. However, TRUST GEEKS HACK EXPERT actual worth went beyond its technical aspects. They offered constant emotional support during the ordeal, understanding my fragility and sense of violation. My tense nerves were calmed by their comforting presence and kind comments, which served as a reminder that I wasn't alone in this struggle. With their help, I was able to reestablish my sense of security and control, which enabled me to return my attention to the significant areas of my life that had been upended. Ultimately, TRUST GEEKS HACK EXPERT all-encompassing strategy not only recovered my online accounts but also my general peace of mind, which is a priceless result for which I am incredibly appreciative of their knowledge and kindness. Make the approach and send a message to TRUST GEEKS HACK EXPERT Via Web site <> www://trustgeekshackexpert.com/-- E>mail: Trustgeekshackexpert(At)fastservice..com -- TeleGram,<> Trustgeekshackexpert
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Being a parent is great until your toddler figures out how to use your devices. One afternoon, I left my phone unattended for just a few minutes rookie mistake of the century. I thought I’d take a quick break, but little did I know that my curious little genius was about to embark on a digital adventure. By the time I came back, I was greeted by two shocking revelations: my toddler had somehow managed to buy a $5 dinosaur toy online and, even more alarmingly, had locked me out of my cryptocurrency wallet holding a hefty $75,000. Yes, you heard that right a dinosaur toy was the least of my worries! At first, I laughed it off. I mean, what toddler doesn’t have a penchant for expensive toys? But then reality set in. I stared at my phone in disbelief, desperately trying to guess whatever random string of gibberish my toddler had typed as a new password. Was it “dinosaur”? Or perhaps “sippy cup”? I felt like I was in a bizarre game of Password Gone Wrong. Every attempt led to failure, and soon the laughter faded, replaced by sheer panic. I was in way over my head, and my heart raced as the countdown of time ticked away. That’s when I decided to take action and turned to Digital Tech Guard Recovery, hoping they could solve the mystery that was my toddler’s handiwork. I explained my predicament, half-expecting them to chuckle at my misfortune, but they were incredibly professional and empathetic. Their confidence put me at ease, and I knew I was in good hands. Contact With WhatsApp: +1 (443) 859 - 2886 Email digital tech guard . com Telegram: digital tech guard recovery . com website link :: https : // digital tech guard . com Their team took on the challenge like pros, employing their advanced techniques to unlock my wallet with a level of skill I can only describe as magical. As I paced around, anxiously waiting for updates, I imagined my toddler inadvertently locking away my life savings forever. But lo and behold, it didn’t take long for Digital Tech Guard Recovery to work their magic. Not only did they recover the $75,000, but they also gave me invaluable tips on securing my wallet better like not leaving it accessible to tiny fingers! Who knew parenting could lead to such dramatic situations? Crisis averted, and I learned my lesson: always keep my devices out of reach of little explorers. If you ever find yourself in a similar predicament whether it’s tech-savvy toddlers or other digital disasters don’t hesitate to reach out to Digital Tech Guard Recovery. They saved my funds and my sanity, proving that no challenge is too great, even when it involves a toddler’s mischievous fingers!
21.11.24 08:02 Emily Hunter

If I hadn't found a review online and filed a complaint via email to support@deftrecoup. com , the people behind this unregulated scheme would have gotten away with leaving me in financial ruins. It was truly the most difficult period of my life.
22.11.24 04:41 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
22.11.24 15:26 cliftonhandyman

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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25.11.24 02:20 briankennedy

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