Machine Learning Catch-Up

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Dataconomy » Machine Learning Newsletter

Big Data News, Events, and Expert Opinion

Excerpts:

How Your Tumblr Activity Gives Away Your Drinking Habits

TumblrAccording to Gartner, 80% of data created today is unstructured. No data could be less structured than the vast ocean of selfies, photos of Starbucks beverages and millennial angst that is Tumblr. But the billion-dollar company may have found a way to take this rich, messy cultural cachet and turn it in to something profitable for big business- by using machine learning to mine its image content for brand sentiment analysis. Tumblr have just inked a deal with Ditto, which will allow Ditto unfettered access to Tumblr’s content to scan for branded products, and sell the results to corporations like Coca-Cola, Jack Daniels- and, of course, the aforementioned Starbucks. David Rose, CEO of Ditto, discussed the deal with Motherboard. “Twitter and Instagram have been suppliers of data to us for...
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Spotify Is Using Deep Learning to Recommend Songs You’ll Love But Never Knew Existed

Spotify Is Used Deep Learning to Recommend Songs You'll Love But Never Knew ExistedSander Dieleman, a Spotify intern and Ph.D. student has published a lengthy report on his grand plans to revolutionise Spotify’s recommender system. In short, he’s planning to use deep learning to recommend you songs that actually sound like what you listen to, meaning it’s not just the most popular songs that will rise to the top of your recommended playlist. Historically, Spotify has used collaborative filtering to recommend songs from its immense musical library. As the blog post states, “The idea of collaborative filtering is to determine the users’ preferences from historical usage data. For example if two users listen to largely the same set of songs, their tastes are probably similar.” This technique hinges on consumption and behaviour patterns, rather than the content itself, and is the principle behind...
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Improving the Steam Recommender System, Emptying Your Wallet

1766783-valve_logo_the_bald_guyWe recently caught up with Kevin Wong, a business intelligence professional and machine learning enthusiast, to talk about his latest project: Building a better recommender system for Steam. For anyone who isn’t familiar, Steam is a digital distribution platform for games, developed by Valve Corporation. It boasts over 75 million members, with between 5 and 8 million online at any time, and estimated revenues of around $1.75 billion. To put those figures in perspective, entertainment giant Netflix only had 50 million members at the end of July. Gabe Newell, MD at Valve, has claimed the company is more profitable per head than either Apple or Google. Clearly, Steam is a force to be reckoned with, so how can one man hope to improve on their product? Hi Kevin, tell us...
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Real Life Analytics Will Identify Shoppers in Less than a Second

20140809-_MG_5246-1024x682The futuristic idea of shops knowing your age, gender and race when you walk into a store and targeting products accordingly is becoming a reality. Real Life Analytics, a MassChallenge startup based out of Boston, have developed computer vision software which can determine your demographic profile within 20 milliseconds of you walking into a store. This idea has left a sour taste in the mouth of some consumers. A similar venture, SceneTap, which aimed to use computer vision to tell you the age and gender breakdown of patrons at a bar you were considering visiting, found themselves having to write an open letter to defend their technology, in which they stressed nobody’s privacy was being infringed. This is something Robert DeFilippi, Real Life Analytics’ co-founder, was also keen to highlight. “We...
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Everstring Lands $12m in Funding to Expand its Data Scientist Workforce

4462688756_e0dba736a0_zBig Data startup Everstring has acquired $12 million in funding which it intends to utilize in enlisting more data scientists to assist enterprises in identifying prospective sales leads and new clients through predictive analytics. The round was led by Lightspeed Venture Partners, with participation from existing investors Sequoia Capital and IDG Ventures. EverString puts software agents into customers’ CRM systems to get information about existing leads, essentially names and websites. Then, it goes around the web to gather information about these businesses. “We use natural language processing to crawl the web and convert the unstructured data into structured data,” said EverString’s CEO Vincent Yang. To determine the attributes of current customers EverString “track[s] 10,000+ indicators”. some of the indicators being, employee size, company revenue, product offering, location, social status, technology,...
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