Posts Tagged ‘Netflix’

Amazon Fires Up Fire TV Featuring Voice Search And Content Viewing Prediction Capabilities

retAmazon today unveiled its Fire TV streaming video device. During the announcement event in Manhattan, company vice president Peter Larsen called the $99 set top box “tiny, incredibly powerful and unbelievably simple.” For users, that power and simplicity are designed to be evident in features such as the device’s ability to project and preload the content users will want to see and to navigate via voice search.

A statement by Amazon founder and CEO Jeff Bezos reads that, “Our exclusive new ASAP (Advanced Streaming and Prediction) feature predicts the shows you’ll want to watch and gets them ready to stream instantly.” Movies or tv shows are buffered for playback before users hit the play button, the company says; those choices are made by analyzing users’ watch lists and recommendations. As users’ viewing habits change, the caching prediction algorithm will adjust accordingly, and personalization capabilities should get better over time as buyers use the Fire TV device.

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Elasticsearch 1.0 Takes Realtime Search To The Next Level

esearchpixElasticSearch 1.0 launches today, combining Elasticsearch realtime search and analytics, Logstash (which helps you take logs and other event data from your systems and store them in a central place), and Kibana (for graphing and analyzing logs) in an end-to-end stack designed to be a complete platform for data interaction. This first major update of the solution that delivers actionable insights in real-time from almost any type of structured and unstructured data source follows on the heels of the release of the commercial monitoring solution Elasticsearch Marvel, which gives users insight into the health of Elasticsearch clusters.

Organizations from Wikimedia to Netflix to Facebook today take advantage of Elasticsearch, which vp of engineering Kevin Kluge says is distinguished by its focus from its open-source start four years ago on realtime search in a distributed fashion. The native JSON and RESTful search tool “has intelligence where when it gets a new field that it hasn’t seen before, it discerns from the content of the field what type of data it is,” he explains. Users can optionally define schemas if they want, or be more freeform and very quickly add new styles of data and still profit from easier management and administration, he says.

Models also exist for using JSON-LD to represent RDF in a manner that can be indexed by Elasticsearch. The BBC World Service Archive prototype, in fact, uses an index based on ElasticSearch and constructed from the RDF data held in a central triple store to make sure its search engine and aggregation pages are quick enough.

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New Fund Ready to Back Big Data Startups


Alexandra Stevenson of The New York Times reports, “You may not admit that you want to watch ‘The Real Housewives of New Jersey,’ but Netflix knows you do. Using algorithms that use search data to predict what television shows people want to watch is one way in which companies are using Big Data to connect the dots. It’s captured the imagination of some of Silicon Valley’s most well-known venture capitalists, who have committed more than $10 million to a new early stage fund to help foster start-ups that analyze behavioral data to determine patterns and make predictions about social behavior.” Read more

Jybe Takes Machine-Learning To Leisure And Entertainment Recommendations

The recommendation problem is a machine-learning problem, says  startup Jybe, and one that it aims to address with its iPhone app that now is in beta. Coming soon (though not immediately) to the iPhone 5, which will require some redesigning to maximize real estate, the mobile app supports earlier iPhones, the iPod touch and iPads running iOS 4.3 or later.

Unlike services such as Yelp, that are more reviews than recommendations, Jybe takes the “serendipitous discovery” approach to real-world goods and services (movies, books, restaurants, and dishes). Founded by CEO Arnab Bhattacharjee, CTO Tim Converse, and chairman of the board Tuoc Vinh Luong, a team with a slate of experience in the search engine industry at names like Yahoo and Powerset, Jybe looks to provide implicit search, i.e. search without query. “The only way to figure out your interests is to figure out who you are, what you like and surface things interesting for you to consume,” says Bhattacharjee.

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Yoke Brings Ontology Graph to Facebook Dating To Reveal Connections, And Make Them, Too

A new matchmaking app from one of the founders of Adaptive Semantics hit Facebook yesterday. Adaptive Semantics, you may recall, developed the JuLiA semantic text-parsing technology that’s now part of AOL’s toolkit, courtesy of its Huffington Post acquisition.

Kingfish Labs is the startup that created Yoke, and it includes Jeff Revesz as CTO. Rob Fishman, who was Huffington Post’s social media editor, is the CEO of the company, which recently received $500,000 in seed funding. Yoke’s take on the online dating scene is to bring people together with the help of an ontology graph: Its algorithms explore entities, the connections between them, and the strength of those connections to discover common interests between people that just might lead to a real-world bond.

Yoke is deeply connected into the Facebook API, Revesz says. With users’ permission, it accesses basic data such as birthday, location, and education history, and also pulls their Likes in music, bands, artists, movies, books and some general areas outside those categories. Ditto for their closest friends (again, with respect to their privacy settings, so no guarantee as to how far it can get for each individual). Behind the scenes, Yoke mashes up its Facebook Graph data with data from Amazon, Netflix, and Echonest (which powers Spotify radio) to produce an ontology of interest entities for connecting users together. These three sources were chosen, Revesz says, because they’re the easiest to work with, the biggest and the best.

“We’re looking both for similarity information and ontology information,” he explains – that is, for example, how closely two movies might resemble each other, and what entities they might share in common, such as the same director or actors. So, if someone likes one particular movie, the ontology of interest entities can be used to show other people who like similar things.

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Facebook’s Timeline Launches

Facebook today posted that users now officially can upgrade their profiles to its Facebook Timeline by heading here. Timeline, as Mark Zuckerberg described in September, is Facebook’s way of helping uses curate the stories of their lives, calling out the most meaningful events and recent highlights. During the F8 Developers’ Conference, he said that Facebook had “rethought from the ground up the heart of the Facebook experience.”

The Open Graph protocol, based on RDFa, provides power to the experience, enabling applications to focus on filling out user Timelines with lightweight activities, and on discovering new things through friends in what Zuckerberg at the time called a frictionless experience. As an example, he noted the debut of the Open Graph Spotify music app that adds to a user’s Timeline the songs she listens to, radio stations, and albums.

Other Open Graph app launch partners announced at the event in the fall were The Daily, Dailymotion, Earbits, The Guardian, Hulu, iHeartRadio, The Independent, Izlesene, Jelli, My Video, Netflix, Rdio, Slacker, Songza, The Washington Post, and Yahoo. Others, such as The Huffington Post, joined later. In late November, Facebook said that the publishers building social news apps to help users see what their friends or reading or to view past top articles are seeing good early results.

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What Have You Liked Today — And What Are You Going To Do About It?

So, how many things have you liked today? Chances are that somewhere in the last 24 hours you’ve given a thumbs-up to a news article you came across on a friend’s Facebook post, a movie on Netflix, or a beer garden on Foursquare.

An application in beta from Cascaad, dubbed CircleMe, hopes to be the single source for hosting and managing all your likes.  “Typically you leave those traces all over the web but they aren’t leveraged,” says Erik Lumer, Cascaad founder and executive chairman. “It’s in your profile somewhere but you’re not getting much out of it.” Lumer says Cascaad is betting there’s value to help users manage the activity on their likes in one place, so that they can get more out of them such as more easily tracking new things underway that are connected to what they already like, or get recommendations from others with similar interests. And to do it with greater permanence, so to speak. As Lumer points out, you can potentially discover a new book on Facebook that one of your friends liked, but “two hours later it’s gone. There are hundreds of messages on top of it. There’s not a clean way to leverage that effectively, so in that sense I think we are very complementary” to Facebook likes.

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Exploring the Semantics of Yahoo Search Direct

A couple of weeks ago Yahoo debuted a beta version of its Search Direct technology, which was seen as a competitor to Google Instant in that it shows search options as users begin typing in a query. There were also questions raised among those in the industry about how this relates to Yahoo’s search deal with Microsoft.  There was less chatter about its semantic footprint, but that’s a question worth addressing as well.

So the Semantic Web Blog took these questions to Raghu Ramakrishnan, chief scientist for Yahoo Search, whose background includes expertise in data management and mining, the cloud, and, of course, the semantic aspects of search. As Ramakrishnan sees it, Search Direct exemplifies Yahoo’s search slogan of providing answers, not links. Meaningful answers, whatever form they take, are the new direction in search, he says, and Yahoo wants compete “at the next level where technology is young and there is room for differentiation and market opportunities in being the best.”

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