The Filter Bubble: How to Fix Content CurationTania Kasongo
Presentation from 2011 Mashable Media Summit by Eli Pariser, Author of The Filter Bubble and President/Founding Board MemberMoveon.org and Accessnow.org
ITS 4차 메인 세미나_알고리즘(배은정, 김용겸, 김성수, 정민영, 유재현)
왓챠(Watcha) 알고리즘 분석(15.11.06)
고려대학교 정보기술경영학회 : ITS
Web: http://itsociety.co.kr/
Mail: president@itsociety.co.kr
How to eliminate ideas as soon as possibleRoman Zykov
Retail Rocket helps web shoppers make better shopping decisions by providing personalized real-time recommendations through multiple channels with over 100MM unique monthly users and 1000+ retail partners. The rapid improvement of the product is important to win on the high-concurrency market of real-time personalization platforms.
The necessity of introducing constant innovations and improvements of algorithms for recommendation systems requires correct tools and a process of rapid testing of hypotheses. It’s not a secret that 9 out of 10 hypotheses actually do not improve the performance at least. We had the task stated as follows: How to detect and eliminate the idea that doesn’t improve as early as possible, to spend a minimum of resources on that process.
In the report we will talk about:
How we make our process of hypotheses testing faster.
One programming language for R&D.
Enmity and friendship of offline and online metrics.
Why it is difficult to predict the impact of changing diversity of algorithms.
What is the benefit of AA/BB online tests.
Bayesian statistics for the evaluation of online tests.
ABOUT THE SPEAKER
Roman Zykov is the Chief Data Scientist at the Retail Rocket. In Retail Rocket is responsible for algorithms of personalized and non-personalized recommendations. Previous to Retail Rocket, Roman was the Head of analytics at the biggest e-commerce companies for almost ten years. He received Ms.Sc. in applied mathematics and physics from the MIPhT in 2004.
Электронная коммерция: от Hadoop к Spark ScalaRoman Zykov
Как обрабатывать большой объем данных быстро с наименьшими затратами? Мы смогли этого добиться в компании
RetailRocket. Обработка данных – это наш бизнес! У нас много данных: более 100 Тбайт, в сутки нам поступает более 100 млн
событий для обработки. До недавнего времени у нас все работало на кластере на базе Hadoop относительно устаревшего
дистрибутива Cloudera CDH 4.5, программный код был написан на Pig, Hive, Python и Java. Это порождало ряд проблем с
архитектурой, производительностью. Тестирование превращалось в настоящую головную боль. В конце лета RetailRocket
перешел на Yarn на базе CDH 5.1.2. Это открыло путь к более совершенным технологиям семейства Spark. Сейчас мы
находимся в фазе полного перехода на Spark на функциональном языке Scala. Это позволило нам избавиться от зоопарка
технологий, упростив архитектуру решений и автоматизировав тестирование. Первые результаты не заставили себя ждать –
получен прирост производительности на том же железе в три-пять раз. А это значит, что мы будем меньше инвестировать в
расширение парка серверов кластера. В докладе будет рассказано о проблемах, с которыми мы столкнулись, и о том как мы
их решили. Будут примеры исходного кода для оптимизации производительности и повышения удобства работы, который мы
закоммитили в наш публичный GitHub
сервисы персонализации на основе данныхRoman Zykov
Персонализация как сервис на основе данных
Персонализация на основе данных о пользователях играет все большую роль в аналитических CRM системах. Самым первым шагом важно понять, какой продукт или сервис на основе данных вы готовы предложить своим клентам. Далее в докладе будут рассмотрены следующие вопросы:
Достаточно ли у вас данных для персонализации продукта?
Как оценить успешность проекта по персонализации?
Как подружить аналитический и операционный CRM?
Какие основные причины провала таких проектов?
Самостоятельная разработка или outsource?
Насколько важен вопрос правильных технологий?
Где место сегментации клиентов?
В докладе будут вкратце разобраны некоторые примеры российских и зарубежных компаний.
Этот доклад я презентовал на конференции BI тренды 11 октября 2012 года в Москве. http://events.cnews.ru/events/programm/bi_instrumenty_v_rossii__poslednie_trendy.shtml
The Filter Bubble: How to Fix Content CurationTania Kasongo
Presentation from 2011 Mashable Media Summit by Eli Pariser, Author of The Filter Bubble and President/Founding Board MemberMoveon.org and Accessnow.org
ITS 4차 메인 세미나_알고리즘(배은정, 김용겸, 김성수, 정민영, 유재현)
왓챠(Watcha) 알고리즘 분석(15.11.06)
고려대학교 정보기술경영학회 : ITS
Web: http://itsociety.co.kr/
Mail: president@itsociety.co.kr
How to eliminate ideas as soon as possibleRoman Zykov
Retail Rocket helps web shoppers make better shopping decisions by providing personalized real-time recommendations through multiple channels with over 100MM unique monthly users and 1000+ retail partners. The rapid improvement of the product is important to win on the high-concurrency market of real-time personalization platforms.
The necessity of introducing constant innovations and improvements of algorithms for recommendation systems requires correct tools and a process of rapid testing of hypotheses. It’s not a secret that 9 out of 10 hypotheses actually do not improve the performance at least. We had the task stated as follows: How to detect and eliminate the idea that doesn’t improve as early as possible, to spend a minimum of resources on that process.
In the report we will talk about:
How we make our process of hypotheses testing faster.
One programming language for R&D.
Enmity and friendship of offline and online metrics.
Why it is difficult to predict the impact of changing diversity of algorithms.
What is the benefit of AA/BB online tests.
Bayesian statistics for the evaluation of online tests.
ABOUT THE SPEAKER
Roman Zykov is the Chief Data Scientist at the Retail Rocket. In Retail Rocket is responsible for algorithms of personalized and non-personalized recommendations. Previous to Retail Rocket, Roman was the Head of analytics at the biggest e-commerce companies for almost ten years. He received Ms.Sc. in applied mathematics and physics from the MIPhT in 2004.
Электронная коммерция: от Hadoop к Spark ScalaRoman Zykov
Как обрабатывать большой объем данных быстро с наименьшими затратами? Мы смогли этого добиться в компании
RetailRocket. Обработка данных – это наш бизнес! У нас много данных: более 100 Тбайт, в сутки нам поступает более 100 млн
событий для обработки. До недавнего времени у нас все работало на кластере на базе Hadoop относительно устаревшего
дистрибутива Cloudera CDH 4.5, программный код был написан на Pig, Hive, Python и Java. Это порождало ряд проблем с
архитектурой, производительностью. Тестирование превращалось в настоящую головную боль. В конце лета RetailRocket
перешел на Yarn на базе CDH 5.1.2. Это открыло путь к более совершенным технологиям семейства Spark. Сейчас мы
находимся в фазе полного перехода на Spark на функциональном языке Scala. Это позволило нам избавиться от зоопарка
технологий, упростив архитектуру решений и автоматизировав тестирование. Первые результаты не заставили себя ждать –
получен прирост производительности на том же железе в три-пять раз. А это значит, что мы будем меньше инвестировать в
расширение парка серверов кластера. В докладе будет рассказано о проблемах, с которыми мы столкнулись, и о том как мы
их решили. Будут примеры исходного кода для оптимизации производительности и повышения удобства работы, который мы
закоммитили в наш публичный GitHub
сервисы персонализации на основе данныхRoman Zykov
Персонализация как сервис на основе данных
Персонализация на основе данных о пользователях играет все большую роль в аналитических CRM системах. Самым первым шагом важно понять, какой продукт или сервис на основе данных вы готовы предложить своим клентам. Далее в докладе будут рассмотрены следующие вопросы:
Достаточно ли у вас данных для персонализации продукта?
Как оценить успешность проекта по персонализации?
Как подружить аналитический и операционный CRM?
Какие основные причины провала таких проектов?
Самостоятельная разработка или outsource?
Насколько важен вопрос правильных технологий?
Где место сегментации клиентов?
В докладе будут вкратце разобраны некоторые примеры российских и зарубежных компаний.
Этот доклад я презентовал на конференции BI тренды 11 октября 2012 года в Москве. http://events.cnews.ru/events/programm/bi_instrumenty_v_rossii__poslednie_trendy.shtml
Experience the thrill of Progressive Puzzle Adventures, like Scavenger Hunt Games and Escape Room Activities combined Solve Treasure Hunt Puzzles online.
Young Tom Selleck: A Journey Through His Early Years and Rise to Stardomgreendigital
Introduction
When one thinks of Hollywood legends, Tom Selleck is a name that comes to mind. Known for his charming smile, rugged good looks. and the iconic mustache that has become synonymous with his persona. Tom Selleck has had a prolific career spanning decades. But, the journey of young Tom Selleck, from his early years to becoming a household name. is a story filled with determination, talent, and a touch of luck. This article delves into young Tom Selleck's life, background, early struggles. and pivotal moments that led to his rise in Hollywood.
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Early Life and Background
Family Roots and Childhood
Thomas William Selleck was born in Detroit, Michigan, on January 29, 1945. He was the second of four children in a close-knit family. His father, Robert Dean Selleck, was a real estate investor and executive. while his mother, Martha Selleck, was a homemaker. The Selleck family relocated to Sherman Oaks, California. when Tom was a child, setting the stage for his future in the entertainment industry.
Education and Early Interests
Growing up, young Tom Selleck was an active and athletic child. He attended Grant High School in Van Nuys, California. where he excelled in sports, particularly basketball. His tall and athletic build made him a standout player, and he earned a basketball scholarship to the University of Southern California (U.S.C.). While at U.S.C., Selleck studied business administration. but his interests shifted toward acting.
Discovery of Acting Passion
Tom Selleck's journey into acting was serendipitous. During his time at U.S.C., a drama coach encouraged him to try acting. This nudge led him to join the Hills Playhouse, where he began honing his craft. Transitioning from an aspiring athlete to an actor took time. but young Tom Selleck became drawn to the performance world.
Early Career Struggles
Breaking Into the Industry
The path to stardom was a challenging one for young Tom Selleck. Like many aspiring actors, he faced many rejections and struggled to find steady work. A series of minor roles and guest appearances on television shows marked his early career. In 1965, he debuted on the syndicated show "The Dating Game." which gave him some exposure but did not lead to immediate success.
The Commercial Breakthrough
During the late 1960s and early 1970s, Selleck began appearing in television commercials. His rugged good looks and charismatic presence made him a popular brand choice. He starred in advertisements for Pepsi-Cola, Revlon, and Close-Up toothpaste. These commercials provided financial stability and helped him gain visibility in the industry.
Struggling Actor in Hollywood
Despite his success in commercials. breaking into large acting roles remained a challenge for young Tom Selleck. He auditioned and took on small parts in T.V. shows and movies. Some of his early television appearances included roles in popular series like Lancer, The F.B.I., and Bracken's World. But, it would take a
Barbie Movie Review - The Astras.pdffffftheastras43
Barbie Movie Review has gotten brilliant surveys for its fun and creative story. Coordinated by Greta Gerwig, it stars Margot Robbie as Barbie and Ryan Gosling as Insight. Critics adore its perky humor, dynamic visuals, and intelligent take on the notorious doll's world. It's lauded for being engaging for both kids and grown-ups. The Astras profoundly prescribes observing the Barbie Review for a delightful and colorful cinematic involvement.https://theastras.com/hca-member-gradebooks/hca-gradebook-barbie/
_7 OTT App Builders to Support the Development of Your Video Applications_.pdfMega P
Due to their ability to produce engaging content more quickly, over-the-top (OTT) app builders have made the process of creating video applications more accessible. The invitation to explore these platforms emphasizes how over-the-top (OTT) applications hold the potential to transform digital entertainment.
Hollywood Actress - The 250 hottest galleryZsolt Nemeth
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At Digidev, we are working to be the leader in interactive streaming platforms of choice by smart device users worldwide.
Our goal is to become the ultimate distribution service of entertainment content. The Digidev application will offer the next generation television highway for users to discover and engage in a variety of content. While also providing a fresh and
innovative approach towards advertainment with vast revenue opportunities. Designed and developed by Joe Q. Bretz
Meet Dinah Mattingly – Larry Bird’s Partner in Life and Loveget joys
Get an intimate look at Dinah Mattingly’s life alongside NBA icon Larry Bird. From their humble beginnings to their life today, discover the love and partnership that have defined their relationship.
Matt Rife Cancels Shows Due to Health Concerns, Reschedules Tour Dates.pdfAzura Everhart
Matt Rife's comedy tour took an unexpected turn. He had to cancel his Bloomington show due to a last-minute medical emergency. Fans in Chicago will also have to wait a bit longer for their laughs, as his shows there are postponed. Rife apologized and assured fans he'd be back on stage soon.
https://www.theurbancrews.com/celeb/matt-rife-cancels-bloomington-show/
240529_Teleprotection Global Market Report 2024.pdfMadhura TBRC
The teleprotection market size has grown
exponentially in recent years. It will grow from
$21.92 billion in 2023 to $28.11 billion in 2024 at a
compound annual growth rate (CAGR) of 28.2%. The
teleprotection market size is expected to see
exponential growth in the next few years. It will grow
to $70.77 billion in 2028 at a compound annual
growth rate (CAGR) of 26.0%.
Modern Radio Frequency Access Control Systems: The Key to Efficiency and SafetyAITIX LLC
Today's fast-paced environment worries companies of all sizes about efficiency and security. Businesses are constantly looking for new and better solutions to solve their problems, whether it's data security or facility access. RFID for access control technologies have revolutionized this.
Orpah Winfrey Dwayne Johnson: Titans of Influence and Inspirationgreendigital
Introduction
In the realm of entertainment, few names resonate as Orpah Winfrey Dwayne Johnson. Both figures have carved unique paths in the industry. achieving unparalleled success and becoming iconic symbols of perseverance, resilience, and inspiration. This article delves into the lives, careers. and enduring legacies of Orpah Winfrey Dwayne Johnson. exploring how their journeys intersect and what we can learn from their remarkable stories.
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Early Life and Backgrounds
Orpah Winfrey: From Humble Beginnings to Media Mogul
Orpah Winfrey, often known as Oprah due to a misspelling on her birth certificate. was born on January 29, 1954, in Kosciusko, Mississippi. Raised in poverty by her grandmother, Winfrey's early life was marked by hardship and adversity. Despite these challenges. she demonstrated a keen intellect and an early talent for public speaking.
Winfrey's journey to success began with a scholarship to Tennessee State University. where she studied communication. Her first job in media was as a co-anchor for the local evening news in Nashville. This role paved the way for her eventual transition to talk show hosting. where she found her true calling.
Dwayne Johnson: From Wrestling Royalty to Hollywood Superstar
Dwayne Johnson, also known by his ring name "The Rock," was born on May 2, 1972, in Hayward, California. He comes from a family of professional wrestlers, with both his father, Rocky Johnson. and his grandfather, Peter Maivia, being notable figures in the wrestling world. Johnson's early life was spent moving between New Zealand and the United States. experiencing a variety of cultural influences.
Before entering the world of professional wrestling. Johnson had aspirations of becoming a professional football player. He played college football at the University of Miami. where he was part of a national championship team. But, injuries curtailed his football career, leading him to follow in his family's footsteps and enter the wrestling ring.
Career Milestones
Orpah Winfrey: The Queen of All Media
Winfrey's career breakthrough came in 1986 when she launched "The Oprah Winfrey Show." The show became a cultural phenomenon. drawing millions of viewers daily and earning many awards. Winfrey's empathetic and candid interviewing style resonated with audiences. helping her tackle diverse and often challenging topics.
Beyond her talk show, Winfrey expanded her empire to include the creation of Harpo Productions. a multimedia production company. She also launched "O, The Oprah Magazine" and OWN: Oprah Winfrey Network, further solidifying her status as a media mogul.
Dwayne Johnson: From The Ring to The Big Screen
Dwayne Johnson's wrestling career took off in the late 1990s. when he became one of the most charismatic and popular figures in WWE. His larger-than-life persona and catchphrases endeared him to fans. making him a household name. But, Johnson had ambitions beyond the wrestling ring.
In the early 20
2. What Is Netflix?
• “Connecting people to the movies they love”
• Online DVD movie rental:
– Users subscribe for a fixed fee per month
• Plans define #movies out at once, #turns in a month
– Find, then queue up movies on website
– USPS delivers DVDs within 1 business day most areas
– Keep as long as you want; no late fees
– Return in pre-paid mailer when done
– Next DVD on your queue sent automatically
• Working on movie delivery over the net
• Choice of 65,000 titles…which ones?
5. Netflix and Cinematch Scale
• 5M active customers
– Ship 1.4M disks per day from 40 locations
• 1.4B ratings since 1997
– 2M ratings per day
– 1B predictions per day
• Item-to-item analysis with many data-
conditioning heuristics
• 2 days to retrain on new ratings
• Manual item setup for “coldstart” titles
– Automatically retired
10. 6000
5000
3000
4000
2000
1000
0
Music & Musicals
Foreign
Drama
* Popular = top 10K by ratings
Documentary
Children & Family
Comedy
Television
Classics
Sports
Action & Adventure
Horror
Special Interest
Thrillers
Anime & Animation
Sci-Fi & Fantasy
Romance
Independent
Gay & Lesbian
Popular
Predictable Films by Genre
Total
Popular
Predictable
11. Climbing Mount Predictable
Predictable movies
9000
8000
7000
6000
Shooting stars
5000 4 and 5 stars
# movies Predictablybad (<3)
4000 Predictable
3000
2000
1000
0
0
25
50
75
100
150
200
300
400
500
600
700
800
900
1000
10000
# user ratings
13. Error by Confidence
Error as confidence increases
1.2
1
0.8
0.6 RMSE
+/- Stars
MAE
0.4 Bias
0.2
0
Average 0 1 2 3
-0.2
14. Does It Matter?
• Absolutely critical to retaining users
– As CM has improved and RMSE has fallen, the
percentage of 4-5 star movies rented has increased
• Important to users:
– There are only so many new releases
– Help jog memories about movies to see
– CM reflects the collective memory of good movies
16. What’s Next?
• Anticipate scale of 20M subscribers in 2010-2012
– Nearly 10B ratings, 10M/day
– 5B predictions/day
• Improved learning algorithms
– Improve coverage, accuracy and learning speed
• Help the non-rater
• Explore getting movie tastes beyond ratings
• Encode traits of movies that predict emotional
response
• Motivate a user to take an unknown but likely great
movie
