dardera reinette fayrlie
Yoshi Heffernan
So I was given the task to create a very simple Netflix based program. So essentially it has to have a few different classes. A movie class (with basic movie info, already done this though) and a user class (this has to have basic user info like name and account number. but also must have a movie list that displays the five most recently watched movies, and a playlist or essentially a Queue that has the next movies they are going to watch) This is where I am stuck currently, because I do not know enough about Queues to make one for this project. So how would I go about creating a queue for this?
A Queue is a data structure that uses the FIFO format. In the Java API a Queue is an interface which you can either implement yourself, or use an object such as a LinkedList which does it for you. Here is some sample code which should show you one way to use an ArrayDeque as a Queue.
Ok, don't shoot the messenger but I was asked to see if I could unblock the queue management area for Netflix but still block the streaming media part of it... We're using the URL filtering capabilities of the PA 2050 device and I have a policy defined that's based on an Active Directory user group to filter traffic. I'm not sure how I would go about doing this, any thoughs?
Its pretty basic, your going to create a rule that precedes your URL filtering rule. The rule will be from trust to untrust application will be "netflix" and action will be drop.
Has Palo Alto changed the Netflix signature recently? In September we had blocked the application per Phil's suggestion earlier in this thread and people were able to login and manage their queue but couldn't view any movies. This morning, though, I wasn't able to login anymore. Thanks --
@cshep: you would have to review all of the release notes to see what has changed between each version of the content update to see if PAN engineering have updated any particular application signature(s).
If you see the block in either the "traffic" or "threat" logs then that would be due to either an application update or an antivirus update. If you see the block in the URL filtering log then it is your URL filtering profiles that need examination.
I would say looking at the logs should give you an indication of whats going on with the block. I have a handful or preset filters for looking at that kind of thing. I'm running 3.1.4 code with the latest app and threat updates and have just noticed I'm unable to get to the netflix.com queue. I can get to the sites front page however loggin in doesn't happen. When I look at the traffic log is see a deny for netflix based on the app, i don't see anything blocked in the URL log for netflix so it's definately the app. I'd have to look back as well but I'm guessing a app and threat update changed something.
If you require assistance resolving this issue I would suggest posting some screen shots of the traffic, URL filtering and threat logs to this thread so that we can do some detective work and find the root cause of the issue.
In 1999, Netflix had 2,600 DVDs to choose from but intended to grow its library to 100,000 titles. To make it easier for members to find movies, Netflix developed a personalized merchandising system. Initially, it focused on DVDs, but in 2007, Netflix launched streaming, which used the same personalization system.
Search. There was little investment in search in the early days as Netflix assumed members searched for expensive new release DVDs. But the team discovered that the titles members chose included lots of older, less expensive, long-tail titles, so they ramped up investment in search.
The high-level engagement metric was retention. However, it takes years to affect this metric. So Netflix had a more sensitive, short-term proxy metric: The percentage of members who rated at least 50 movies during their first two months with the service.
The theory: members would rate lots of movies to get better recommendations. Many ratings from a member signaled they appreciated the personalized recommendations they received in return for their ratings.
It took Netflix more than a decade to demonstrate that a personalized experience improved retention. But consistent growth in this proxy convinced the company to keep doubling down on personalization.
Over time, Netflix got better at suggesting similar titles for members to add to their queue, which drove this source from ten to fifteen percent of total queue adds. The QUACL was a great test environment for algorithm testing. In fact, Netflix executed its first machine learning tests within the QUACL.
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Gib\u2019s note: Welcome to the 200 new members who joined since my last essay! After five months, we\u2019re 5,400 strong. In each essay, I draw from my experience as both VP of Product at Netflix and Chief Product Officer at Chegg to help product leaders build their careers. This is essay #50.
Netflix began as a DVD-by-mail startup, following the invention of the DVD player in 1996. In 1998 Netflix launched its website with less than 1,000 DVDs. Here\u2019s what the site looked like its first few years:
In twenty years, Netflix has gone from members choosing 2% of the movies the merchandising system suggests to 80% today. And the system also saves members\u2019 time. In the early days, a member would explore hundreds of titles before finding something they liked. Today most members look at forty choices before they hit the \u201Cplay\u201D button. Twenty years from now, Netflix hopes to play that one choice that\u2019s \u201Cjust right\u201D with no browsing required.
Below, I detail Netflix\u2019s progress from the launch of Cinematch in 2000 to 2006. It\u2019s a messy journey, with an evolving personalization strategy propelled by Netflix\u2019s ability to execute high-cadence experiments using its home-grown A/B test system.
Netflix introduced a personalized movie recommendation system, using member ratings to predict how much a member would like a movie. The algorithm was called Cinematch, and it\u2019s a collaborative filtering algorithm.
Netflix created a five-star rating system and eventually collected billions of ratings from its members. Netflix experimented with multiple \u201Cstar bars,\u201D at times stacking the stars to indicate expected rating, average rating, and friends\u2019 rating. (It was a mess.)
Dynamic store. This algorithm indicated if the DVD was available to merchandise. Late in the DVD era, the algorithm also determined if a DVD was available in a member\u2019s local hub. (By 2008, Netflix only merchandised titles that were available locally to increase the likelihood of next-day DVD delivery.)
Recognizing multiple family members used a shared account, Netflix launched \u201CProfiles.\u201D This feature enabled each family member to generate its own movie list. It was a highly requested feature, but only two percent of members used it despite aggressive promotion. It was a lot of work to manage an ordered list of DVDs, and only one person in each household was willing to do this.
Given the low adoption, Netflix announced its plan to kill the feature but capitulated in the face of member backlash. A small set of users cared deeply about the feature\u2014 they were afraid that losing Profiles would ruin their marriages! As an example of \u201Call members are not created equal,\u201D half the Netflix board used the feature.
The hypothesis: if you create a network of friends within Netflix, they\u2019ll suggest great movie ideas to each other and won\u2019t quit the service because they don\u2019t want to leave their friends. At launch, 2% of Netflix members connected with at least one friend, but this metric never moved beyond 5%.
Create algorithms and presentation layer tactics to connect members with movies they\u2019ll love. Use the explicit/implicit taste data, along with lots of data about movies and TV shows (ratings, genres, synopsis, lead actors, directors, etc.), to create algorithms that connect members with titles. Also, create a UI that provides visual support for personalized choices.
Why the 2011 dip in the metric? By this time, most members streamed movies, and Netflix had a strong implicit signal about member taste. Once you hit the \u201CPlay\u201D button, you either kept watching or stopped. Netflix no longer needed to collect as many star ratings.
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