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A Guide To Social Media Algorithms & How They Work

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A Guide To Social Media Algorithms & How They Work

Why do so many marketers keep asking, “How do social media algorithms work?” Because the algorithms for the major platforms can change quickly.

But, marketers should also keep asking, “Which social media platforms have the most users?” Because that data can change frequently, as well.

So, here are the latest answers to the first question about the algorithms for the eight platforms that you should be considering today.

Spoiler alert: This update contains some surprising shifts in the latest data on monthly unique visitors, monthly visits, and monthly average visit duration from SimilarWeb.

How Does The YouTube Algorithm Work?

YouTube got 1.953 billion unique visitors worldwide in May 2022. The platform received 35.083 billion monthly visits that month with an average visit duration of 21:41.

Now, some social media marketers may be shocked, shocked to find YouTube ranking ahead of Facebook.

But, SimilarWeb’s data above is only for desktop and mobile web channels. It doesn’t include data for connected TVs, which became the fastest-growing screen among YouTube viewers in 2020.

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This makes it imperative to know how YouTube’s algorithm works.

YouTube’s algorithm tries to match each viewer to the videos they’re most likely to watch and enjoy. But, with over 500 hours of video content uploaded every minute, this is a Herculean task.

YouTube’s search and discovery systems tackle this challenge by paying close attention to:

  • What viewers watch.
  • What they don’t watch.
  • How much time do they spend watching?
  • What do they share and like?

Next, you need to learn that YouTube has multiple algorithms, including ones for:

  • YouTube Search: Videos are ranked based on how well titles, descriptions, and video content match the viewer’s search and which videos get the most engagement for a search.
  • Up Next: The ranking of suggested videos is based on machine learning’s understanding of which ones viewers are most likely to watch next. These videos are often related to the video a viewer is watching, but they can also be personalized based on the viewer’s watch history.
  • Your homepage: Videos are selected based on how often viewers watch a channel or topic, how well similar videos have interested and satisfied similar viewers, and how many times YouTube has already shown each video to a viewer.
  • YouTube Shorts: YouTube wants both short and long videos to succeed. So, relative watch time is generally more important for short videos, while absolute watch time is generally more important for longer videos.

So, what should you do next?

First, read my column, How To Optimize YouTube Videos To Help Ukraine, which provides tips on keyword research, title optimization, writing descriptions, custom thumbnails, and other video SEO best practices.

Next, read Jon Clark’s article, 13 Key Elements Of Successful YouTube Videos. He focuses on how to make a great video.

Why is that important? Because YouTube’s search and discovery system “finds” videos for each viewer and their varying interests in order to get them to watch more videos that they’ll enjoy so they’ll come back to YouTube regularly.

How Does The Facebook Algorithm Work?

Facebook got only 1.620 billion unique visitors worldwide in May. The platform received 19.739 billion visits that month with an average session duration of 10:05.

Now, Facebook’s unique visitors started dipping worldwide in February 2022.

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But, as you can see in the chart below, there was a substantial drop in unique visitors in Russia in early March, after Russia blocked Facebook in an effort to control the spread of information on the invasion of Ukraine.

Screenshot courtesy of Similarweb, June 2022

This had a negative impact on Facebook’s total unique visitors worldwide, which were already losing momentum. Nevertheless, the platform is still too big to ignore.

So, how does Facebook’s algorithm work today?

Well, we knew how Facebook’s News Feed ranking process worked in December 2021 when Anna Stepanov, Head of Facebook App Integrity, wrote a post that said:

“News Feed uses personalized ranking, which takes into account thousands of unique signals to understand what’s most meaningful to you. Our aim isn’t to keep you scrolling on Facebook for hours on end, but to give you an enjoyable experience that you want to return to.”

And she summarized half a dozen of the biggest changes Facebook had made in 2021 to give users more control over, and insight into, how content appears in their News Feed.

This included publishing a new series of Widely Viewed Content Reports to share what content is seen by the most people in News Feed in the U.S.

Ironically, Facebook’s latest Widely Viewed Content Report showed the top four domains in Q4 2021:

  • youtube.com (168.1 million content viewers).
  • media1.tenor.co (118.4 million).
  • gofundme.com (112.4 million).
  • tiktok.com (105.0 million).

But, then in February 2022, Matt G. Southern reported Facebook Shifts Focus To Short-Form Video After Stock Plunge. And on June 16, 2022, Southern reported Facebook To Restructure Main Feed Around Video Content.

So, what should you do next? First, read Southern’s stories and learn why Tom Alison, head of Facebook, plans to turn its main feed into a “discovery engine” for video content.

According to Alison, the main tab in the Facebook app will become a mix of Stories and Reels at the top, followed by posts that its discovery engine will recommend from across both Facebook and Instagram.

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Next, follow Southern’s expert, authoritative, and trustworthy advice:

“The best way to prepare for this change, if Facebook is a priority for you and your business, is to get comfortable with creating and publishing more short form video. While Facebook will continue to surface text and photo posts, they’ll be ancillary to the main attractions of Reels and Stories.”

How Does The Instagram Algorithm Work?

Instagram got 1.050 billion unique visitors worldwide in May. The platform received 6.497 billion visits that month with an average session duration of 07:51.

Russia has also banned Instagram, but the growth in unique visitors from other countries around the world has offset that.

So, you still need to know how Instagram’s algorithms work.

In June 2021, Adam Mosseri, the head of Instagram, wrote a post entitled, Shedding More Light On How Instagram Works. He revealed:

“Instagram doesn’t have one algorithm that oversees what people do and don’t see on the app. We use a variety of algorithms, classifiers, and processes, each with its own purpose.”

For the Feed and Stories, the key ranking signals are:

  • Information about the post: How popular a post is, when it was posted, how long it is, if it’s a video, and if it’s attached to a location.
  • Information about the person who posted: How many times users have interacted with that person in the past few weeks.
  • User activity: What a user might be interested in and how many posts they’ve liked.
  • User history of interacting with someone: How interested a user is in seeing posts from a particular person.

For Explore, the key ranking signals are:

  • Information about the post: How popular a post seems to be as well as how many and how quickly other people are liking, commenting, sharing, and saving a post.
  • User history of interacting with someone: (See above.)
  • User activity: What posts a user has liked, saved, or commented on as well as how they’ve interacted with posts in Explore in the past.
  • Information about the person who posted: (See above.)

For Reels, the key ranking signals are:

  • User activity: Which Reels a user has liked, commented on, and engaged with recently.
  • User history of interacting with someone: (See above.)
  • Information about the reel: The audio track, video data such as pixels and whole frames, as well as popularity.
  • Information about the person who posted: (See above.)

So, each part of the app uses similar ranking signals, but their order of importance varies. Mosseri explained:

“People tend to look for their closest friends in Stories, but they want to discover something entirely new in Explore. We rank things differently in different parts of the app, based on how people use them.”

For more tips and advice, read the article by Shelley Walsh entitled, 22 Ways To Get More Instagram Followers Right Now. Then, read Amanda DiSilvestro’s article, How To Use Instagram Reels For Business.

How Does The Twitter Algorithm Work?

Twitter got 979 million unique visitors worldwide in May. The platform received 7.056 billion visits that month with an average session duration of 10.39.

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This data does not screen for fake or spam accounts. Nevertheless, it’s worth investing the time and effort to keep up with how Twitter’s algorithm works.

Like most social media platforms, Twitter has multiple algorithms.

Twitter says its “algorithmic Home timeline displays a stream of Tweets from accounts you have chosen to follow on Twitter, as well as recommendations of other content we think you might be interested in based on accounts you interact with frequently, Tweets you engage with, and more.”

If users want to, they can click on the star symbol to see the latest Tweets as they happen. But, few people choose to drink water from a firehose.

If they want to, users can click on “Explore” and see Trending tweets or ones about COVID-19, News, Sports, and Entertainment.

If users want to, they can click on “More” to see the Topics that Twitter thinks they’re interested in.

Like most social media platforms, Twitter’s algorithms use machine learning to sort content based on different ranking signals.

And it’s worth noting that Twitter is currently involved in analyzing the results of its algorithms as part of its “responsible machine learning initiative.”

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Here’s what Twitter has said publicly about its Home timeline, Trends, and Topics ranking signals:

Relevance:

  • ​​Users’ previous actions on Twitter, like their own Tweets and Tweets they’ve engaged with.
  • Accounts they often engage with.
  • Topics they follow and engage with most.
  • The number of Tweets related to a topic.
  • For Trends: their location.

Engagement:

  • For Tweets: “How popular it is and how people in your network are interacting with [the Tweet].”
  • For Trends: “The number of Tweets related to the Trend.”
  • For Topics: “How much people are Tweeting, Retweeting, replying, and liking Tweets about that Topic.”

Recency:

  • For Trends: “Topics that are popular now, rather than topics that have been popular for a while or on a daily basis.”

Rich Media:

  • The type of media the Tweet includes like an image, video, GIF, and polls.

For more advice and tips, read Lisa Buyer’s article, 8 Terrific Tips To Optimize A Twitter Business Or Brand Profile. Then, read the article by Julia McCoy entitled, How To Be A Top Tweeter: 10 Tips That Will Get Your Tweets Noticed.

How Does The TikTok Algorithm Work?

TikTok got 690 million monthly visitors worldwide in May. The platform received 1.766 billion visits that month with an average session duration of 03:48.

This data doesn’t include Douyin.com, which is counted separately. But, as the chart below illustrates, TikTok.com gets about 98% of the unique visitors worldwide for both of the ByteDance apps.

TikTok.com gets about 98% of the unique visitors worldwideScreenshot courtesy of Similarweb, June 2022

So, you should probably learn how TikTok’s algorithm works ASAP.

In June 2020, TikTok revealed how its recommendation system selected videos in a post entitled, How TikTok recommends videos #ForYou.

Little has fundamentally changed since then, except the U.S. government is no longer trying to ban the social media platform.

TikTok’s For You feed presents a stream of videos curated to each user’s interests, making it easy for a user to find content and creators they love.

In other words, there isn’t one For You feed for over one billion monthly active TikTok users. There are a billion For You feeds tailored to what each user watches, likes, and shares.

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TikTok added, “This feed is powered by a recommendation system that delivers content to each user that is likely to be of interest to that particular user.”

And recommendations are based on a number of factors, including:

  • User interactions such as the videos they like or share, accounts they follow, comments they post, and content they create.
  • Video information, which might include details like captions, sounds, and hashtags.
  • Device and account settings like their language preference, country setting, and device type.

TikTok also revealed:

“All these factors are processed by our recommendation system and weighted based on their value to a user. A strong indicator of interest, such as whether a user finishes watching a longer video from beginning to end, would receive greater weight than a weak indicator, such as whether the video’s viewer and creator are both in the same country.

Videos are then ranked to determine the likelihood of a user’s interest in a piece of content, and delivered to each unique For You feed.”

On the other hand, TikTok said:

“While a video is likely to receive more views if posted by an account that has more followers, by virtue of that account having built up a larger follower base, neither follower count nor whether the account has had previous high-performing videos are direct factors in the recommendation system.”

So, what should you do next? First, read Miranda Miller’s article, 40+ TikTok Stats Digital Marketers Need To Know. Then, read my column, How TikTok’s Search Algorithms Power Content Discovery.

How Does The Pinterest Algorithm Work?

Pinterest got 409 million unique visitors worldwide in May. The platform received 945 million visits that month with an average session duration of 05:29.

With Instagram declaring it is “no longer just a square photo-sharing app,” this is the time to learn how Pinterest’s algorithm works.

The ranking factors on Pinterest relate more to engagement metrics and social shares, but it also involves keywords.

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And Pinterest autocomplete provides ideas by automatically suggesting semantically related modifiers to a core keyword.

Pinterest’s search feature then curates a user’s “feed” based on what they’re searching for and how those key terms are used in the Pins being shared by content creators.

Pinterest also categorizes and sub-categorizes topics to make it easy to find keywords for your particular niche.

To optimize your Pins:

  • Use long images: The optimal Pin size is 1,000 by 1,500 px or a ratio of 2:3.
  • Use eye-catching colors: Catch users’ attention and stand out with high-contrast colors.
  • Use enticing, keyword-rich titles: Entice users to click through to your content.
  • Use detailed descriptions: Include your target keywords in your descriptions.

Then, optimize your boards. Boards provide a great opportunity to tell Pinterest’s search engine how you categorize your products and/or organize your content, which will only aid visibility.

Finally, aim for engagement, which can increase your Pin’s (and your profile’s) visibility in search, increasing your traffic.

For additional information and advice, read Southern’s story, Pinterest Updates Algorithm To Surface More Content Types. Then, read Jessica Foster’s article, 12 Pinterest SEO Tips For High-Traffic Success.

How Does The LinkedIn Algorithm Work?

LinkedIn got 306 million unique visitors worldwide in May. The platform received 1.479 billion visits that month with an average session duration of 07:32.

So, social media marketers – especially ones at B2B organizations – need to know how LinkedIn’s algorithm works.

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In June 2019, Pete Davies, Senior Director of Product Management at LinkedIn, wrote a post entitled, What’s in your LinkedIn Feed: People You Know, Talking about Things You Care About. He explained, “The more valuable the conversation, the higher in your feed the post will be.”

How does LinkedIn’s algorithm know if a conversation is valuable? It uses the following framework:

  • People you know: LinkedIn’s algorithm looks at a user’s connections and prioritizes who they’ve interacted with directly through comments and reactions; the user’s implicit interests and experiences based on information in their profile; explicit signals, such as who a user works with; as well as who would benefit from hearing from the user.
  • Talking about: A lot of sophistication goes into understanding a good conversation. As a rule of thumb, better conversations are authentic and have a constructive back and forth.
  • Things you care about: LinkedIn’s algorithm also looks at whether the content and the conversation are relevant and interesting to a user. It considers a number of signals, including joining groups and following hashtags, people, and pages.

So, what should you do next? First, read Jessica Foster’s article, How The LinkedIn Algorithm Works & Optimizing For It. Then, read Matt G. Southern’s article, LinkedIn Debunks Algorithm Myths In New Video Series.

How Does The Reddit Algorithm Work?

Reddit got 237 million unique visitors worldwide in May. The platform received 1.669 billion visits that month with an average session duration of 09:59.

With Facebook setting its sights on video to regain its momentum, this is a good time to learn how Reddit’s algorithm works.

In June 2021, the official blog for Reddit posted Evolving the Best Sort for Reddit’s Home Feed. It provided insights into how Reddit determines which relevant posts to show users.

The post revealed that:

“Reddit’s systems build a list of potential candidate posts from multiple sources, pass the posts through multiple filtering steps, then rank the posts according to the specified sorting method. Over the years, we’ve built many options to choose from when it comes to sorting your Home feed.”

Here’s how each sort option recommends content:

  • “Hot” ranks using votes and post age.
  • “New” displays the most recently published posts.
  • “Top” shows users the highest vote count posts from a specified time range.
  • “Controversial” shows posts with both high count upvotes and downvotes.
  • “Rising” populates posts with lots of recent votes and comments.
  • ‘Best” uses machine learning algorithms to personalize the order in which users see posts.

For more tips and information, read the article by Brent Csutoras entitled, A Beginner’s Guide To Reddit: How To Get Started & Be Successful. Then, read Southern’s story, Reddit Makes Comments Searchable.

Why Should You Keep Asking Questions?

The latest data from SimilarWeb indicates that you should continue asking “Which social media platforms have the most users?” as well as “How do social media algorithms work?”

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Things change too quickly and frequently in this particular arena for anyone to think that past performance is even remotely indicative of future results.

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AI Re-Ranking For Semantic Search

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AI Re-Ranking For Semantic Search

Search isn’t just about matching keywords – and that’s even more true when we talk about semantic search.

Semantic search is about finding the right information for the searcher at the right time.

That goes beyond finding the right keywords and concepts and speculating how searchers will interact with the results.

Artificial intelligence (AI) re-ranking will take information about the people who come to search and tailor search results to the individual.

That might be done on a cohort level, changing results based on trends, seasonality, and popularity.

It might also be done individually, changing results based on the current searcher’s desires.

While AI re-ranking is not easy to implement in a search engine, it brings outsized value for conversions and searcher satisfaction.

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Re-Ranking With Artificial Intelligence

AI-driven re-ranking can improve search results, no matter the underlying ranking algorithm a search engine uses.

That’s because good search results are more than textual relevance and business metrics like raw popularity.

Good results take into account other signals and do so on a per-query level.

To see why this is important, let’s focus on the business metric of popularity.

It’s a good general ranking signal but can fall short for specific queries. A search query of “red dress” might bring up in the first results two different dresses: “backless dress with red accents” and “summer dress in bright red.”

The backless dress might be more popular as an overall dress and product.

But in this case, specifically, it’s not what customers want.

They want a red dress, not one with red accents, and they click and buy accordingly.

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Shouldn’t the search engine take that as a signal to rank the summer dress higher?

Search Analytics

As the above example shows: Understanding what searchers are doing is necessary for re-ranking.

The two most common events to track are clicks and conversions.

Generally, those are the only two events necessary and must be events coming from search.

The example above also highlights another important consideration: the events should be tied to specific queries.

That allows the search engine to learn from the interplay between the different result sets and user interactions. It propels the summer dress higher in the search results for the “red dress” query.

The same product might be less popular for other queries than its neighbors.

When looking at your different events, you’ll want to weigh them differently, too.

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Clicking on a result is a sign of interest while making a purchase (or any other conversion metric) is a sign of commitment.

The ranking should reflect that.

The weighting doesn’t need to be complex.

You can go as simple as saying that conversions are worth double clicks.

You should test the right ratio for your own search.

You may also want to discount events based on the result ranking at the time the searcher saw it.

We know that a result’s position influences its clickthrough rate (CTR).

Without discounting events, you may have a situation where the top results become even more entrenched because they get more interactions, which keep them ranked higher – and repeating infinitely.

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Freshness And Seasonality

A simple way to combat this self-reinforcing loop is by discounting events based on the time passed since the event.

That happens because each event that occurred in the past has an increasingly small impact on re-ranking. That is, until, at some point, it has no impact at all.

For example, you might divide the impact of each event by two, each day, for 30 days. And after 30 days, stop using the event for ranking.

A nice benefit of using freshness in the re-ranking algorithm is that it also introduces seasonality into the results.

Not only do you stop recommending videos that were extremely popular years ago but are boring to people today; you also will recommend “learn how to swim” videos in the summer, and “learn to ski” videos in the winter.

YouTube has seasonality and freshness built into its algorithm precisely for this purpose.

Using Signals To Re-rank

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Now that you’ve got the signals and decaying them over time, you can apply them to the search results.

When we see “artificial intelligence,” we often think of something incredibly complex and inscrutable.

AI, though, can also be as simple as taking data over time and using it to make decisions, like we’re doing here.

One easy approach is to take a certain number of results and simply re-rank them based on a score.

For performance reasons, this number of results will generally be fairly small (10, maybe 20). Then, rank them by score.

As we discussed above, the score could be as simple as adding up the number of conversions times two, plus the number of clicks.

Adding a decay function makes for more complexity, as does discounting based on result position – but the same general principle applies.

Learning To Rank

A drawback of this re-ranking system is that you are limited to re-ranking a smaller number of results.

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If you have a result that would otherwise be popular but isn’t ranking high, that result won’t get the attention it warrants.

This system also requires events on the records and the queries you want to re-rank.

It won’t work for brand new product launches or user-generated content (UGC) that often comes in and out of the search index.

Learning to rank (LTR) can address these issues.

Much like the re-ranking we’ve discussed above, LTR also works based on the idea that the records searchers interact with are better than the ones they don’t.

The previous re-ranking method works by boosting or burying results directly when tied to a specific query.

Meanwhile, LTR is much more flexible. It works by boosting or burying results based on other popular results.

LTR uses machine learning to understand which queries are similar (e.g., “video games” and “gaming console”).

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It can then re-rank results on the less popular queries based on interactions on the more common ones.

LTR doesn’t only generalize on queries; it generalizes on records, too.

The LTR model learns that a certain type of result is popular; for example, the Nintendo Switch game “Legend of Zelda: Breath of the Wild.”

Then, it can start to connect to other similar results (for example, “Legend of Zelda: Skyward Sword”) and boost those.

Why, then, not just use LTR if it appears to be much more powerful than your typical re-ranking and provides more query and record coverage?

(In other words: It generalizes better.)

In short, LTR is much more complex and needs more specialized in-house machine learning (ML) expertise.

Additionally, understanding why certain results are ranked in certain places is more difficult.

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With the first type of re-ranking, you could look at the number of clicks and conversions over time for one record compared to another.

Meanwhile, with LTR, you have an ML model that makes connections that may not always be obvious.

(Are “Breath of the Wild” and “Sonic Colors” really all that similar?)

Personalization

While re-ranking works across all searchers, personalization is what it sounds like: personal.

The goal of personalization is to take results that are already relevant and re-rank them based on personal tastes.

While there is a debate on how much web search engines like Google use personalization in their results, personalization often impacts the performance of results in on-site search engines.

It is a useful mechanism for increasing search interactions and conversions from search.

Search Analytics

Just as with re-ranking, personalization depends on understanding how users interact with search results.

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By tracking clicks and conversions, you’ll have a clearer idea of the kinds of results that the user wants to see.

One significant difference between re-ranking and personalization on this front is that, depending on your search, you might want to adjust how you apply personalization.

For example, if you sell groceries, you definitely want to recommend previously purchased products.

But if your website sells books, you won’t want to recommend a book that a customer has already bought. Indeed, you may even want to move those books down in the search results.

It’s also true, however, that you shouldn’t push personalization so hard that users only see what they’ve interacted with before.

Search empowers both finding and discovery. So, if they return to the search bar, you should be open to the possibility that they want to see something new.

Don’t rank results exclusively via personalization; make it a mix with other ranking signals.

Just as with re-ranking, personalization also benefits from event decay.

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Decreasing the impact of older events makes a search more accurately represent a user’s current tastes.

In a way, you can think of it as personal seasonality.

Personalization Across Users

The kind of personalization we’ve seen so far is based on an individual’s own interactions, but you can also combine it with what others are doing inside search.

This approach shows an outsized impact on situations where the user hasn’t interacted with the items in the search results before.

Because the user doesn’t interact with the search result items, you can’t boost or bury based on past interactions, by definition.

Instead, you can look at users that are similar to the current user and then personalize based on what they have interacted with.

For example, say you have a user who has never come to you for dresses but has purchased many handbags.

Then, you can look for other users who have similar tastes and have also interacted with dresses.

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Intuitively, other customers who like the same type of handbags as our searcher should also like the same dresses.

Re-Ranking And Personalization For Discovery

Search is only one example of where re-ranking and personalization can make an impact. You can use these same tools for discovery as well.

The secret is to think of your home page and category pages as search results.

Then, it’s clear that you can use the same tools you use for search and gain the same benefits.

For example, a home page is similar to a search page without a query, isn’t it? And a category landing page sure does look like a search page with a category filter applied to it.

If you add personalization and re-ranking to these pages, they can be less static. They will serve users what they prefer to see, and they can push items higher that are more popular with customers overall.

And don’t worry, personalization and re-ranking can mix with editorial decisions on these pages or inside search.

The best way to handle this is by fixing the desired results in certain places and re-rank around them.

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We’ve seen that personalization and re-ranking are two approaches that take user interactions with relevant signals to make search better.

You can let your user base influence the result by using the interactions.

Little by little, these interactions tell the search engine what items should be ranking higher.

Ultimately, searchers benefit from a better search experience, and you benefit from more clicks and conversions.

More resources:


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