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Laurene Arrison
I have cleared cache, checked for updates in both Roku and Netflix, as well as uninstalled and reinstalled the app. However whenever I try to watch a new show or start a show over, it crashes. I have a 50 in Roku tv.
65" Roku/Sharp and nothing has helped. Anything Netflix crashes seconds into it. Sometimes, the TV freezes and reboots. Tried the cheat code, uninstalling and reinstalling, unplugging, and Netflix never works. Everything else does. Meanwhile, all other TVs in the house are fine, whether it's a Roku, Firestick, or Chromecast. I am putting a Chromecast on this TV and giving up. It's not worth the headache anymore.
This past Black Friday 11/24/23 (the real one - not the "ALL MONTH BF"), I started looking for Android/Google TV's just for the heck of it, to see what might be coming on CYBER MONDAY 11/27/23.
Just for kicks, I went back to my ROKU Phillips TV (PHIL65ROKU) to look at the reviews. I even went to look at a few other models and yet I could not find anything on this subject. To my surprise, I didn't find any negative reviews about this issue, so I wrote my own review. It would be great if everyone in this room went back to their purchase (most likely on Walmart or Amazon) and write something up if you haven't already done so. For those who do not like to write reviews or simply do not have the time, you are welcome to copy, edit and paste mine, as follows:
(Title) ---> MOST with ROKU streaming NETFLIX and PRIME CRASH
Purchased 3/1/23 and ever since, Netflix and Prime will Crash intermittently. Upon finding ROKU's community I found this topic: Prime and Netflix crashing on Roku TV on 4/9/23 which unfortunately was after my purchase date and return window. Turns out, there are many with various ROKU models that suffer the same issue. DO YOURSELVES A FAVOR, read these discussions prior to purchasing ROKU built in to any TV! I advise you to spend a little bit more and get yourselves an Android TV or Google TV!! Don't say I didn't warn you. There's a reason why ROKU TV's are so inexpensive. Those interested, here's the link:
-and-Netflix-crashing-on-Roku-TV/td-p/867893
As I stated here previously, I'm finished posting updates about my headaches with this TV. However, I simply had to come back and post an update of what I did while shopping for an ANDROID/GOOGLE TV.
This isn't the first time I threw away money on something and I doubt it will be the last. I spent $309 (wo/tax) and got 9 months of viewing with 8 1/2 months of headaches with this issue. It's time to spend yet more $$$ but this time for the Android or Google TV. Such a waste and more stuff to give away or throw away (hopefully the former, so as not to add to the landfill).
Thanks again for all those that contributed to this topic. At least I feel a bit better knowing I wasn't singled out. Anyone talking "Class Action Suit"? I might join up with RocketLawyer (as I have other Legal activities needing attention). If I do, I might just bring this up to them and see what they offer. What the heck, I'm going to put my contact info here for those who want to join my list: totec...@yahoo.com
I look forward to hearing from you.
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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