For the third time Netflix organized its Personalization, Recommendation and Search Workshop. It was awesome to get invited for this event during my tech holiday in the San Francisco Bay Area. The experienced data scientists from all over Silicon Valley and beyond made it a knowledge-rich day. Google, Microsoft, Netflix, Spotify, and University of Minnesota shared how to understand and serve your users better. There was one subject that all speakers agreed on: classic matrix factorization (collaborative filtering) reached its expiration date. This includes challenges of multi-armed bandits, an implicit feedback approach, top-N ranking techniques, tyranny of the majority and algorithmic bias. At Netflix almost your whole homepage is personalized: the banner, carousels, order, artwork, text and search.
Human Factors in Hypertext (HUMAN) Opinion Mining, Summarization and Diversification Narrative and Hypertext I attended the Opinion Mining, Summarization and Diversification workshop. The workshop started with a talk titled: "On Reviews, Ratings and Collaborative Filtering," presented by Dr. Oren Sar Shalom, principal data scientist at Intuit, Israel. Next, Ophélie Fraisier, a PhD student studying stance analysis on social media at Paul Sabatier University, France, presented: "Politics on Twitter: A Panorama," in which she surveyed methods of analyzing tweets to study and detect polarization and stances, as well as election prediction and political engagement. He showed how collective opinion mining can help capture the drivers behind opinions as opposed to individual opinion mining (or sentiment) which identifies single individual attitudes toward an item. I thank a million people! https://t.co/I3quPp6nw3 He also discussed a phenomenon in which people are likely to lie to pollsters (social desirability bias) but are honest to Google ("Digital Truth Serum") because Google incentivizes telling the truth. The paper sessions followed the keynote with two full papers and a short paper presentation. Google search data as "digital truth serum" - while reporting of child abuse go down at the recession time, Google search data indicates that real child abuse increases https://t.co/DQQoAotZqB However, it feels more like a research talk rather than a #keynote.
Entertainment companies are entering the Age of Data, where they'll have access to more information than ever about their products, their audiences and how to create, market and distribute one to the other. Now, those companies and their leadership have to be ready to embrace the coming huge opportunities, especially as data-driven competitors such as Netflix, MoviePass and Amazon transform the industry. That was one message this morning from Stephen F. DeAngelis, CEO and founder of AI provider Enterra Solutions, speaking before a group of Hollywood technology executives in Beverly Hills. He noted wryly that Hollywood has portrayed AI technologies in dark or at least complicated ways over the years, from the murderous HAL 9000 in 2001: A Space Odyssey to the world-ending SkyNet in the Terminator films to the runaway AIs of Ex Machina and Her. We're quite a ways still from AI with that kind of power and autonomy, DeAngelis said, but he cautioned that people think of AI tools in overly limited ways.
It is almost customary to mention the 1995 Hollywood blockbuster, Clueless, while writing about wardrobe management apps. The movie's protagonist, Cher Horowitz (Alicia Silverstone) picked her outfit of the day from a digital wardrobe that captured the imagination of fashionistas galore. Entrepreneurs, mainly in the US, have tried their hand at wardrobe fashion apps but didn't succeed, with the exception of long-standing players like Stylebook and Cladwell. The biggest peeve has been the effort and time taken to get started with such apps. Many users switch off at the prospect of individually photographing every garment and adding its details to the online wardrobe.
What if I tell you that Netflix is NOT in the business of media entertainment? Netflix not only has the largest worldwide subscriber base of any business but managed to keep growing it by 25% last year. Its market capitalization competes head to head with Disney, the most-valued entertainment company in the world. Netflix success story can not be explained without understanding their granular knowledge of their subscriber base and their AI driven focus on personalization. Netflix not only looks at millions of ratings, searches and "plays" a day, but the entire viewing history of billions of hours of content streamed per month.
In today's world, every customer is faced with multiple choices. For example, If I'm looking for a book to read without any specific idea of what I want, there's a wide range of possibilities how my search might pan out. I might waste a lot of time browsing around on the internet and trawling through various sites hoping to strike gold. I might look for recommendations from other people. But if there was a site or app which could recommend me books based on what I have read previously, that would be a massive help. Instead of wasting time on various sites, I could just log in and voila! 10 recommended books tailored to my taste. This is what recommendation engines do and their power is being harnessed by most businesses these days. From Amazon to Netflix, Google to Goodreads, recommendation engines are one of the most widely used applications of machine learning techniques. In this article, we will cover various types of recommendation engine algorithms and fundamentals of creating them in Python. We will also see the mathematics behind the workings of these algorithms. Finally, we will create our own recommendation engine using matrix factorization.
The world's most powerful person used to be Vladimir Putin. This year he was defeated by the Chinese president, Xi Jinping, according to the Forbes ranking of powerful people who make you question what you've been doing with your life so far. It's safe to say that Mr. Putin knows plenty about power, and he believes that advances in artificial intelligence (AI) will not only change the world as we know it, but the global balance of power as well. It looks like other world leaders agree, with China and the US fighting for AI supremacy and the EU scrambling to catch up. Real growth is fueled by cold hard cash, so we've put together a list of the 10 biggest artificial intelligence startups in the world by funding.
The significant advancements made over the last decade in the abilities and cost of parallel computing, algorithms, big data and the transfer to the cloud is expected to bring artificial intelligence (AI) out of laboratories and into the mainstream world. AI is broadly defined as any intelligence demonstrated by machines or software. If a machine is capable of doing more than processing data, for example deriving knowledge from it, and augmenting human decision making, it can be considered as AI. According to data published by Grand View Research, the global artificial intelligence market is projected to reach USD 35.87 billion by 2025, while growing at a CAGR of 57.2 percent. A report by Goldman Sachs emphasized the sweeping changes AI is already causing across a variety of sectors, including advertising (programmatic ad buying), retail (customized recommendations) and investing.
The recommendation engine is one of the biggest martech innovations of the last few years, and it's shaping our entire digital experience. For example, let's say I visit the Harvard Business Review website and read about manufacturing marketing tactics (as one does). When I fire up Netflix, I know that my experience is going to be managed in part because Netflix knows I've been binging on the show Haven and it's going to suggest other supernatural crime fighting series. The development of recommendation engine technology has brought digital personalization to a whole new level. For brands developing content marketing and martech strategies, it's critical to pay attention to this emerging technology and understand where trends are headed when shaping the user experience.
In this AI based Science article, we explore How Netflix adopted an Open Source Model to improve their Entertainment Recommender Systems. First, let us discuss in brief, what Machine Learning basically means. In simple terms, Machine Learning is a technique by which a computer can "learn" from data, without using a complex set of different rules. This approach is mainly based on training a model from datasets. The better the quality of the datasets, the better the accuracy of the Machine Learning Model.