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Facebook published an article that explains how the Facebook News Feed algorithm works. Compared with Facebook’s news feed algorithm patent, both documents explain much about how Facebook ranks posts in the news feed.

Machine Learning and Ranking

Facebook’s news feed algorithm is a machine learning ranking system. It’s not just one algorithm though. It’s a combination of multiple algorithms that work together in different phases.

All of those different layers are applied in order to predict what a Facebook member is going to find relevant to them.

The goal of the algorithms is to to rank which posts show up in the news feed, the order they are in and to select the posts that a Facebook member is likely to be interested in and to interact with.

It’s not just a few signals either that are considered. Facebook states that they use thousands of signals.

“For each person on Facebook, there are thousands of signals that we need to evaluate to determine what that person might find most relevant… to predict what each of those people wants to see in their feed…”

Facebook News Feed Ranking Signals Characteristics of a Facebook Post

One of the ranking signals that Facebook discusses is the “characteristics” of a post.

Facebook is using a feature or quality of a post and determining whether this is the kind of thing that a user tends to interact with more.

For example, if a post is accompanied with a colorful image and a member has a history of interacting with posts with colorful images, then that’s going to be ranked higher.

If a post is accompanied by a video and that’s what a Facebook member likes to interact with, then that’s going to be ranked higher for that member.

Whether the post has an image, a video, if friends of a user are tagged in the post, those and other characteristics of a post are used as a ranking factors for determining whether a post is going to be shown to a user and how high it’s going to be ranked in the news feed.

Facebook used the example of a fictional user called Juan (the name “John” in Spanish) to illustrate the characteristics ranking factor.

This is what Facebook said about the characteristics ranking factor:

If Juan has engaged with more video content than photos in the past, the like prediction for Wei’s photo of his cocker spaniel might be fairly low.

In this case, our ranking algorithm would rank Saanvi’s running video higher than Wei’s dog photo because it predicts a higher probability that Juan would like it.”

Time is a Facebook Ranking Factor

What’s interesting about the example of the fictional “Juan” is that Facebook mentioned that when a post was made is a ranking factor.

The Facebook news feed patent is called, Selection and Presentation of News Stories Identifying External Content to Social Networking System Users.

This is what the Facebook News Feed patent says:

“…news stories may be ranked based on chronological data associated with interactions with the news stories, so that the most recently shared news stories have a higher ranking.”

That seems to confirm the value in posting the same post more than once during the course of a day. It may reach different people across time periods and those who interact with the post may help it to be shown to their friends, etc.

Engagement and Interest

Another ranking factor involves predicting whether a user will be likely to be interested in or engage with a post. Facebook uses a number of signals to make that prediction.

The article is clear on that point:

“…the system determines which posts show up in your News Feed, and in what order, by predicting what you’re most likely to be interested in or engage with.”

And some of those factors that Facebook uses are signals from past posts and people that the user has interacted with. Facebook uses these past interactions to help it predict what a user will interact with in the future.

“These predictions are based on a variety of factors, including what and whom you’ve followed, liked, or engaged with recently.”

Each of these forms of engagement receive a ranking score and are subsequently ranked.

To summarize, the ranking process begins by identifying candidate posts to rank, from a pool of posts that were made since the user’s last login.

The next step is to assign ranking scores to each post.

This is how Facebook explains it by using an example of a fictional user named Juan:

“Next, the system needs to score each post for a variety of factors, such as the type of post, similarity to other items, and how much the post matches what Juan tends to interact with.

To calculate this for more than 1,000 posts, for each of the billions of users — all in real time — we run these models for all candidate stories in parallel on multiple machines, called predictors.”

Ranking Signals are Personalized to the User

An interesting insight into ranking factors is that they are weighted differently from one user to the next. Weighted means for when a ranking signal is more important than another ranking signal.

What Facebook revealed is that for one person, the prediction that they would “like” a post could have a stronger influence on whether that post is ranked.

“Next is the main scoring pass, where most of the personalization happens.

Here, a score for each story is calculated independently, and then all 500 posts are put in order by score.

Any action a person rarely engages in (for instance, a like prediction that’s very close to zero) automatically gets a minimal role in ranking, as the predicted value is very low.”

What that means is that in order for a post to be successful, the post must inspire different forms of engagement from every user.

Contextual Features for Diversity of News Feed

The last step in the ranking process is to ensure diversity of the type of content that is shown in the news feed. That way the user’s feed doesn’t become repetitive.

Multiple Personalized Facebook Ranking Factors

Facebook didn’t list every ranking factor used to rank posts in a news feed. But they did give an idea, an overview of how the ranking process happens and what kinds of behavior are prioritized. We also learned that ranking signals are dynamic and can be weighted differently depending on the person.

Citations

How Does News Feed Predict What You Want to See?

How Machine Learning Powers Facebook’s News Feed Ranking Algorithm

Selection and Presentation of News Stories Identifying External Content to Social Networking System Users (PDF)

Sentiment Polarity for Users of a Social Networking System (PDF)

Re-Ranking Story Content (PDF)

Resolving Entities from Multiple Data Sources for Assistant Systems (PDF)

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## Pinterest Updates Algorithm To Surface More Content Types

Pinterest is introducing a new ranking model to its home feed in an effort to surface certain types of content more often.

While that model is effective at maximizing user engagement, it’s not the best model for surfacing a variety of content types.

But that doesn’t necessarily mean they wouldn’t engage with video content if it were to be surfaced.

Pinterest found itself with a problem of wanting to boost more content types while still keeping content recommendations relevant.

To solve this problem, Pinterest is introducing a real-time ranking system for its home feed called “controllable distribution.”

Controllable Distribution

Pinterest describes controllable distribution as a “flexible real-time system.”

It’s not a complete algorithm overhaul. Rather, controllable distribution is only applied after the traditional home feed ranking algorithm.

Controllable distribution makes it possible to specify a target for how many impressions a certain content type should receive.

For example, controllable distribution could be used to specify that 4% of users’ home feeds should contain video content.

This is done through a system that tracks what percentage of the feed was video in the past. Then, the system boosts or demotes content according to how close that percentage is to the specified target.

Pinterest says this can be accomplished while still respecting users’ content preferences.

What Does This Mean for Marketers?

As a real-time system, the controllable distribution model will be continuously adjusted.

On one hand, that means the home feed won’t get stale for users.

On the other hand, it’s not exactly possible to optimize for an algorithm that changes in realtime.

Pinterest is diversifying the types of content in the home feed. If you want more opportunities to show up in peoples’ feeds then diversify the types of content you publish.

For example, if you only publish photos, then consider adding some videos or GIFs to the mix. Maybe some product pins if you’re an e-commerce retailer.

Pinterest’s target for displaying certain types content will be changing all the time.

Publishing a wide variety of content will help ensure you have the right type of content available at the time Pinterest wants to display it.

Related: 25 Facts You Need to Know About Pinterest

Pinterest’s home feed ranking team used to do manually what controllable distribution is designed to do algorithmically.

Yes, Pinterest’s home feed ranking team actually used to step in and adjust how often certain types of content appeared in users’ home feed.

Yaron Greif of Pinterest’s home feed ranking team describes the old process as “painful for both practical and theoretical reasons.”

“In practice, these hand-tuned boosts quickly became unmanageable and interfered with each other. And worse, they often stop working over time — especially when ranking models are updated. We regularly had to delay very promising new ranking models because they broke business constraints.

In theory, controlling content on a per-request basis is undesirable because it prevents personalization. If we show each user the same number of video Pins we can’t show more videos to people who really like to watch videos or vice versa.”

Pinterest says it’s committed to investing in the post-ranking stage of surfacing content. So it’s possible we may see this model applied elsewhere on the platform in the future.

Source: Medium

Related: 10 Tips to Get More Followers on Pinterest

## 7 Insights Into How Google Ranks Websites

Google’s algorithm is built around understanding content and search queries and making the answers accessible to users in the most convenient manner.

These seven insights show how to develop a winning SEO and content strategy by leveraging what we know about Google’s algorithms.

The following are insights developed by studying patents and research papers published by Google itself.

Insight 1: Follow the Correct Intent

There are some content writing systems that mine the top-ranked websites and provide content writing and keyword suggestions based on the analysis of the top ten to top thirty webpages.

Some people who have used the software have told me that the information isn’t always helpful. And that’s not surprising because mining all of the top-ranked webpages in any given search results page (SERP) is going to result in a noisy data set that’s inaccurate and is of limited usefulness.

One of the issues with identifying user intent is that almost every query contains multiple user intents.

Google solves this problem by showing links to webpages about the most popular user intents first.

For example, in a research study about automatically classifying YouTube channels (PDF), the researchers discuss the role of user intent in determining which results to show first.

In the below quote, where it uses the word “entity,” it’s a reference to what you normally think of as a noun (a person, a place, or a thing):

“A mapping from names to entities has been built by analyzing Google Search logs, and, in particular, by analyzing the web queries people are using to get to the Wikipedia article for a given entity…

For instance, this table maps the name Jaguar to the entity Jaguar car with a probability of around 45 % and to the entity Jaguar animal with a probability of around 35%.”

In plain English, that means researchers discovered that 45% of people who search for Jaguar are looking for information about the automobile and 35% are looking for information about the animal.

That’s user intent that is segmented by popularity.

The takeaway here is that if your content is about selling a product and the top-ranked pages are about how to make that product then it may be possible that the popular user intent for that keyword is how to make that product and not where to buy that product.

That insight may mean that new content is needed to target the underlying “how to make” latent question that is inherent in that search query.

Insight 2: Link Ecosystem Has Changed

Blogging was at an all-time high twelve years ago. Many people were going online to churn out content and link out to interesting websites.

Aside from the recipe niche, that is no longer the case and that may be affecting the link signal that Google uses for ranking purposes. This is super important to think about.

Fewer People Searching for WordPress

There are fewer and fewer people searching for WordPress every year. This indicates that WordPress is declining in popularity in the general population.

The search volume for the keyword “WordPress” has declined by 71% since September 2011.

Fewer People Searching for Blogs

It’s not just WordPress usage that is going down. There are also fewer people searching for blogs, with a pattern that mirrors the decline in searches for WordPress.

The Link Ecosystem in Decline

There may be many reasons why blogging has declined in popularity.

It could be social media or it could be the introduction of the iPhone and Android changed how the public interacts online.

The Link Ecosystem Has Declined

One thing that is indisputable is that fewer people are blogging and the link ecosystem has suffered a strong decline. What caused it is beside the point.

Gary Illyes of Google confirmed that the motivation for turning the nofollow link attribute directive into a hint was so that Google can use those links for ranking purposes.

“Yes. They had been missing important data that links had, due to nofollow. They can provide better search results now that they consider rel=nofollowed links into consideration.”

It’s not unreasonable to consider the use of nofollow links for ranking purposes was done because there are fewer natural links being generated.

With fewer links being naturally generated, it is highly likely that it’s going to affect how websites are ranked and that Google would be increasingly selective about the links it uses.

Today, it is increasingly clear that link strategies that rely on blog links are more easily detected as spam since fewer people are creating blogs.

The takeaway here is that when creating a link building strategy, it’s important to be aware that the link ecosystem is in decline.

That means that freely given natural links are also in decline.

Link strategies must be more creative in terms of identifying who is left linking to websites and understanding why they are linking to websites.

The time for being selective about getting links from so-called “authority” sites is long past.

Get what you can get as long as it is natural and freely given by any relevant website.

Insight 3: Link Drought Link Building Strategy

Because there are fewer natural links being freely given it’s time to rethink the race to obtain the right anchor text and massive amounts of links.

While a freely given link with a relevant anchor text is useful it’s rarely going to happen naturally.

So maybe it’s time to move away from old traditional link building focused on anchor text and guest posting (which today means paid links).

Instead, it may be useful to cultivate links from news and magazines, relevant organizations, and some educational organizations.

Now more than ever it’s time to focus on outreach regardless of whether the outreach results in links. Just take the traffic.

Insight 4: Search Results Show What People Want to See

Ever walk down a supermarket cereal aisle and note how many sugar-laden kinds of cereal line the shelves? That’s user satisfaction in action. People expect to see sugar bomb cereals in their cereal aisle and supermarkets satisfy that user intent.

I often look at the Fruit Loops on the cereal aisle and think, “Who eats that stuff?” Apparently, a lot of people do, that’s why the box is on the supermarket shelf – because people expect to see it there.

Google is doing the same thing as the supermarket. Google is showing the results that are most likely to satisfy users, just like that cereal aisle.

Sometimes, that means showing newbie 101 level answers. Sometimes that means showing something incredibly racist and sad.

For example, in 2009, Google had to apologize for showing an image of Michelle Obama that was altered to resemble a monkey every time someone searched on her name.

Why did Google show that result? Because most people searching on the name Michelle Obama were the kind of people who were satisfied seeing an image of her that resembled a monkey.

Remember those sugar-laden cereals in the supermarket? That’s what those kinds of results are. It’s what I refer to as a “Fruit Loops algorithm,” a popularity-based algorithm that gives users what they expect to see.

Satisfying user intent is what Google means when they talk about showing relevant results. In the old days, it meant showing webpages that contained the keywords that a user typed. Now it means showing the webpage that most users expect to see.

Essentially, the search results pages are similar to the cereal aisle at your supermarket. That’s not a criticism, it’s an observation.

I think it’s useful to think of the search results as a supermarket aisle and considering what kind of “cereal” is most popular. It may influence your content strategy in a positive way.

Insight 5: Expand the Range of Content

Google’s search results are biased to show the content that users expect to see.

This is why Google shows YouTube videos in the search results. It’s what people want to see.

It’s why Google shows featured snippets, it’s what satisfies the most people today who use mobile phones.

It’s not entirely accurate to complain that Google’s search results favor YouTube videos. People find video content useful, particularly for the how-to type of content. That’s why Google shows it.

It’s a bias in the search results, yes. But it’s a reflection of the users’ bias, not Google’s bias.

So if the user has a bias that favors YouTube videos, what should your online strategy response be?

Write more content and build links to it? Or is the proper response to shift to the kind of content users want, in this case, video?

So if you see the search results are favoring a certain kind of content, pivot to producing that kind of content.

Learn to read the room in terms of what users want by paying close attention to what Google is ranking.

Insight 6: Drops in Ranking and NLP

Drops in ranking can sometimes be explained by a shift in how Google interprets what users mean when they search for something.

Google is increasingly using Natural Language Processing (NLP) algorithms which influences what Google believes users want when they search for something.

For example, I witnessed a near rewrite of what kind of content ranked at the top in a certain niche. Informational content zipped to the top, commercial content dropped to the bottom of the top 10.

There was nothing wrong with the commercial sites that dropped, other than how Google understood user intent changed.

Trying to “fix” the commercial sites by adding more links, disavowing links, or adding more keywords to the page is unlikely to help the rankings.

Fixing something that isn’t broken never helps.

That’s why sometimes, it’s a good idea to study the search results first when diagnosing why a site lost ranking.

There might not be anything to fix. But there may be changes needing to be considered.

If your site has dropped in rankings, review what Google is ranking.

If the kinds of sites still ranking feature different content (focus, topic, etc) then the reason why your site dropped may not be about something that’s wrong.

It may be about something that needs changing.

This is why I use the phrase “Fruit Loops Algo” to refer to Google’s user-intent-focused algorithm. It’s not meant as a slur. It’s meant to illustrate the reality of how Google’s search engine works.

Many people want Fruit Loops and Captain Crunch breakfast cereals. The supermarkets respond by giving consumers what they want.

Search algorithms can operate in a similar manner.

A Better Definition of Relevance

That’s not keyword relevance to search terms you’re looking at — it’s relevance to what most users are expecting to see.

Sometimes that is expressed in how many links a site receives.

“Internet search engines aim to identify documents or other items that are relevant to a user’s needs and to present the documents or items in a manner that is most useful to the user. Such activity often involves a fair amount of mind-reading—inferring from various clues what the user wants.

Understanding user intent is so important that Google and other search engines have developed eye-tracking and viewport time technologies to measure where on a search result mobile users are lingering. This helps to measure user satisfaction and understand user intent for mobile users.

Is Google or the User Biased Toward Brands?

Some people believe that Google has a big brand bias. But that’s not it at all.

If you consider this in light of what we know about Google’s algorithm and how it tries to satisfy user intent, then you will understand that if Google shows a big brand it’s because that is what users expect to see.

If you want to change that situation then you must create a campaign to build awareness for your site so that users begin to expect to see your site at the top.

Yes, links play a role in that. But other factors such as what users type into search engines also play a role.

Someone once argued that Google should show results about the river when someone typed Amazon into Google. But that is unreasonable if what most people expect to see is Amazon the shopping site.

Again, Google is not matching keywords in that search query. Google is identifying the user intent and showing users what they want to see.

Key Takeaways Understand the Search Results

The 10 links are not ordered by which page has the best on-page SEO or the most links. Those 10 links are ordered by user intent.

Write for User Intent

Understand what users want to accomplish and make that the focus of the content. Too often publishers write content focused on keywords, what some refer to as “semantically rich” content.

In 2023 I published an article about User Experience Marketing in which I proposed that focusing on user intent will put you in line with how Google ranks websites.

•What task or goal is the content helping the site visitor accomplish?”

Understand Content Popularity

Content popularity is about writing content that can be understood by the widest audience possible. That means paying attention to the minimum grade level necessary for understanding your content.

If the grade level is high, this means your content may be too difficult for some users to understand.

I am not saying that Google prefers sites that a sixth-grader can understand. I am only stating that if you want to make your site easily understood by search engines and the most users, then paying attention to the difficulty of your content may be useful.

Google is not a keyword-matching search engine. Google is arguably a User Intent Matching Engine. Knowing and understanding this will improve everyone’s SEO.

There is a profound insight into understanding this and adapting your search marketing strategy to it.

Use What Is Known About Google’s Ranking Algorithms

Google publishes an astonishing amount of information about the algorithms used to rank websites. There are many other research papers that Google does not acknowledge whether or not the technology is in use.

One can level up their SEO and marketing success by knowing what algorithms Google has admitted to using and what kinds of algorithms have been researched.

More Resources:

Featured image: Master1305/Shutterstock

## Apriori Algorithm: What & How To Use Of The Apriori Algorithm?

Overviews for Apriori Algorithm

The Apriori algorithm is an interesting approach to know what we need to purchase or tell the suggestions of our need. We all know that there is some kind of approach available on the e-commerce platform. It’s none other than that, Amazon, Flipkart, Snapdeal, etc. When we try to purchase an item in e-shopping, the application will give us suggestions that we may buy together. It predicts other customers who frequently buy things together. This algorithm also allows us to know the prediction of things in multiple approaches.

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“Apriori algorithm is an approach to identify the frequent itemset mining using association rule learning over the dataset and finds the trends over data.”

What is the Use of the Apriori Algorithm?

Apriori algorithm works based on conditional rules, and it is considered as a classic algorithm among mining algorithms. Apriori helps to work efficiently by carrying out the mining association rules. Other traditional algorithms had a bottleneck in itemset generation and faced high consumption in time. The main use of this algorithm to mine the dataset by enhancing the user interest and identify the importance of itemsets and generate the frequent occurrences of an itemset. It follows certain approaches,

1. Handles and ready are the datasets

2. Applies mining association rule

Identify frequent itemset and generates a set of data.

Creates rules to find an efficient association.

3. Explore the interpretations using histograms, graphical representations.

Importance of Apriori Algorithm

Increases the efficiency of search assumptions

Enhances the performance of frequent set identification

Transaction reduction is improvised – eliminates the less frequent sets in subsequent scans

Includes hash-based counting.

Eases the construction of user interests.

Identifies the importance of different itemsets.

The support function helps to identify different types of importance in itemsets.

Storage space is reduced with the help of unnecessary itemset reduction.

Improvised accuracy and efficiency of the algorithm.

Works on supervised learning.

Different approaches in different languages

Apriori algorithm in data mining can be achieved in different languages like Python, R, etc. The main role of the algorithm is to find an association rule efficiently. And it is considered as the primary rule of the mining. The requisites of the association rules are,

Finding the possible ways or rules holding its support value greater than its threshold support

And its confidence values more than threshold confidence.

In Python, the papers have been accomplished in two possible ways. They are,

Using the Brute force method – This is a longer process. First, rules are listed out and identify the support & confidence level on each rule. Then eliminates the value which is below its threshold support & confidence.

Using 2 – Step method – This process is much better than Brute force. The first step identifies the frequencies of items and forms a table. As a result, itemsets are found greater than threshold support. The second step uses binary partition on frequent sets and creates rules called candidate rules.

In the R language, there are projects discussed in public forums. Some of the techniques are discussed here.

“Apriori’s approach is an iterative approach, where it uses k-item set to search (k+1) itemsets. So the first itemset is found by gathering the count of each itemset. So it uses 1st itemset to find 2nd and goes on till no itemset can be explored.

An itemset is called a mathematical set of products in a basket.”

Step #1 – Build the data and make it structural for data analysis. For Eg: We can take a comic book store as a case study.

Step #2 – The .csv file is used containing book details of the Comic book store. And the most interesting part is, we are using DC and Marvel collections for data mining.

Step #3 – For the Apriori algorithm, R provides a package called “arules”. This package allows us to compute and inspect the algorithm’s computation. To install and load the package from CRAN.

Step #4 – When we execute apriori’s function, a class is created with the set of parameters. They are Support, Confidence, and Lift.

Here we can set the parameter as NULL or set with support = 0.001 as minimum value & confidence between 0.75 and 0.9. This change in support & confidence will lead to varied results.

Support: It is the basic probability of an event to occur. An event to get a product A, Support(A) is the no.of.transactions including A divided by total transactions.

Confidence: It is the conditional probability of the occurrence in the event. The change that happens in product A had already happened in product B.

Step #5 – List the top 10 rules to know the significant associations.

Step #6 – Let’s interpret the rules using visualizations.

To visualize the apriori association, the “arulesViz” package is used.

A Grouped Matrix of association rules

A Graph model

We can see that customer’s transactions are strongly associated with GSM based on homo/hetro characters. We can also see the EYE and HAIR are strongly associated together.

We can also see that customer buy books who has brown eyes with neutral characters.

Applications using the Apriori algorithm

Used in the health industry – detects patient’s drugs by grouping on ADRs cause on their characteristics.

E-Commerce retail shops.

Used in hydrological systems – predicting natural phenomena.

Used for diabetic study.

Student’s course selection in the E-Learning platform.

Used in Stock management.

Conclusion Recommended Articles

This is a guide to the Apriori Algorithm. Here we discuss What is the Use of the Apriori Algorithm along with the importance and Different approaches. You may also have a look at the following articles to learn more –

## Concept: Taking Tvos Even Further With Handoff, Apple News, A Content

tvOS is missing a lot of tent pole features of the Apple ecosystem. It’s an excellent foundation, but it could use a lot of work. It often feels like tvOS gets left behind when Apple is working on its annual platform updates. It’s time for it to get a chance to shine.

The first part of tvOS that you see is the Home Screen. Apple’s kept it fairly simple for a few years now. It has a large header space for dynamic content and a sea of app icons below it. In fact, it’s been that way since 2012 before Apple officially branded Apple TV’s software as “tvOS.”

There are a couple of ways Apple could improve upon the Home Screen on Apple TV. We can look to other popular smart tv platforms and even the modern iOS Home Screen with widgets for some inspiration.

The Home Screen

Apple has teetered between prioritizing the TV app and the Home Screen on Apple TV for a few years now. I think it’s time to combine them into one system. Across the top, you’d get all of your key categories in addition to a few new ones: on the far left, a search button, and on the far right, a settings button.

In the middle of the Home Screen, the dynamic content header would still be present. But Apple could use it to show more types of information, including news. It also should be detached from the app that’s currently selected. Rather, it should show curated updates from the iTunes team. Below your dock of five chosen favorite apps, you’d find all of the great content in the TV app’s watch now tab.

Additional tabs include a dedicated place for all of your games and a new live tab for content that’s streamed live over the web.

The New Library

Services that integrate with the TV app would be allowed to show favorite shows and movies right in the library alongside your purchases. There’d also be a new podcasts category to display episodes of shows you subscribe to.

Apple News

One thing that’s always felt de-emphasized on tvOS is news. Apple News is such a perfect service to bring over to the platform. Videos could be curated by the Apple News team from articles and served up in a neat feed. Those who subscribe to live news services could access ones that choose to integrate with Apple News.

Apple News Audio and Apple News+ exclusive stories could also be included in a tvOS app. It would introduce a whole new avenue for Apple News content.

Introducing Scenes

Apple TV screensavers are a staple of the platform. Everyone loves the beautiful landscapes, cityscapes, and nature videos shot by Apple. A new ‘scenes’ app could let you play these for extended periods of time in your home, in an office, in a lobby, wherever you want to.

You could save your favorites, set timers, and even have the scene show things like a clock. Newer Apple TV models could even show simultaneous streams of different streams on the selection menu.

Secure Authentication

The new iMac shows that Apple can indeed implement wireless Touch ID. While the new Apple TV remote doesn’t have a Touch ID sensor on it, it certainly could in the future. But Apple could introduce more secure authentication right away with special secure connections to your other Apple devices with Touch ID and Face ID.

When you go to sign into an app or make a purchase, your Apple TV should ask your iPhone, iPad, or Mac to do the authentication work. You could scan your face or fingerprint on your other devices to pay for things quickly and securely.

Handoff

Another feature that could integrate with your other Apple devices is handoff. It’s an obvious feature to bring to Apple TV, and it’s frankly bizarre that it hasn’t already been brought to the platform. When watching a show, movie, playing music, or a podcast, the Apple TV could recognize it and offer you a dismissible menu to transfer the progress to your television seamlessly.

It could work the same way it does between Macs and iOS devices or even like it does with proximity sensing with HomePod, where you can pass audio between devices.

More to Love

Home app Home controls are already available on Apple TV through the control center, but it could be super useful to have a dedicated app for those actions.

Breathe app Fitness+ already integrates with Apple Watch to track your status during classes. Apple could introduce a complementary Breathe app on the tv so you can do synced meditations as well.

Night shift Lots of folks watch television late at night; a yellow filter could help reduce eye strain for late-night viewing or long periods of time.

Rebrand mirroring as Sidecar When using AirPlay mirroring with a Mac, Apple could rebrand it as sidecar and improve latency to make it more on par with iPad.

FTC: We use income earning auto affiliate links. More.

## Facebook Announces Plan To Allow Content Creators To Beg For Donations

Facebook announced an initiative called Gaming Creator Pilot Program that will help video game live-content creators accept donations from their large followings. In exchange, Facebook will control every aspect of the live streaming gaming community, from the software that powers it to how the community is monetized. Specifically, this is a program that allows video gamers to live stream their gaming events and possibly profit. The move amplifies the Internet trend separating content producers from the platform on which their content lives.

Facebook Democratizing Content Creation

“There’s a lot of work to be done, but we’re committed to building the fundamental architecture that gaming creators need to be successful, starting with foundational elements like enabling all creators in the program to livestream in 1080p/60fps. Most of all, with each new feature we add for gaming video, we’re committed to building it alongside our creators hand-in-hand.

To that end, many gaming creators monetize their videos directly from the support of their passionate fans, and we’re actively exploring ways for fans to back their favorite gaming creators via payments during select livestreams on chúng tôi Based on the results of our initial tests, we’ll expand our fan support monetization initiatives to more gaming creators, including participants in our initial pilot program.”

This is part of an ongoing trend where content is increasingly concentrated within the walls of a few organizations such as Facebook, YouTube, Instagram and Twitter. Google has recently introduced a new program called Google Bulletin that allows citizens to publish local news, which can be seen as a part of the trend to remove control of content from traditional publishers and concentrate it within the hands of a large organization. What Facebook is doing fits into this trend of democratizing the creation of content while also walling it off and controlling everything about it, including how it is monetized.

What Does this Mean to Web Publishers?

Short term it’s relatively harmless. Long term, an argument could be made that Facebook is participating in the slow motion disruption of how users interact with the Internet. Facebook is seemingly on a march toward becoming the Internet itself. For example, Facebook groups are where people discuss issues and topics today, largely occupying the niche web forums used to cover.

Is Facebook Turning Publishers into Internet Panhandlers?

Here are the four goals Facebook outlined for it’s live video gaming platform:

To own the platform on which the communities live

Use Facebook’s resources to promote these communities to Facebook, Instagram and Oculus

Introduce Facebook owned tools to assist content creators in monetizing their content

Build a Facebook owned platform that allows anyone to become a content creator

What Does this Mean to Facebook Advertisers?

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