What are Recommendation Algorithms?

Recommendation algorithms have become increasingly popular in today’s digital and paid social media world, from personalised music playlists, product recommendations, Netflix suggestions and even the content you are shown on social media platforms. These recommendation algorithms are designed to provide personalised recommendations based on user behaviour, preferences, and previous interactions and engagements. They are sometimes referred to as content-first algorithms and have been attributed to the success of TikTok and its most popular influencers.

It is fair to say that this new way of delivering content to users has completely transformed the social media landscape. Since the arrival of TikTok and its algorithm, which has made famous people who have gone viral over pieces of content without having to rely on already having thousands of millions of followers, other companies such as Meta and Twitter have introduced Reels and For You pages in order to retain users who may have left for the Chinese company. This alone is proof that recommendation algorithms are here to stay.

Here at Embryo, our paid social team are well aware of the rise of these content-first algorithms and the need to create content that satisfies them. While growing an online following is important, today, content comes first. Get that right, and the following and brand awareness will follow. If you want to learn more about paid social and how our campaigns keep up to date with the latest updates to the major platforms to ensure results then get in touch today. You can call us on 0161 327 3625 or email [email protected].

So, What Exactly Is a Recommendation Algorithm?

A recommendation algorithm is a combination of using a dataset and machine learning that provides personalised recommendations based on users’ historic data. The recommended algorithm is used in recommender systems as is a type of data filtering that helps to predict user preferences and interests, making it easier to find content or products they might like or be interested in. Rather than showing content you’ve chosen to see, such as the pictures and posts of people you follow, these kinds of algorithms show content they think you’ll be interested in based on previous interactions.

How Do Recommendation Algorithms Work?

Most algorithmic recommendations are calculated using the following steps to provide relevant items and resources to individual users based on their previous interactions.

• Step 1 – Collection
• Step 2 – Storage
• Step 3 – Analysis
• Step 4 – Filtering

Since recommendation algorithms use a combination of data and machine learning, these recommender systems are becoming a lot more common on platforms we use in everyday life and are starting to use a variety of different methods to offer recommendations.

Collaborative Filtering

Collaborative filtering is a type of recommendation algorithm used in recommender systems that recommend items to users based on the preferences of similar users. The premise behind collaborative filtering is that people who have similar preferences or behaviours are likely to have similar opinions on items they have not yet interacted with. This type of algorithm first builds a model of the user-item interactions or ratings, then it looks for users with similar behaviour patterns and recommends items that those similar users have shown an interest in but that the target user has not yet seen or interacted with.

Content-Based Filtering

Content-based filtering is a type of recommendation algorithm used in recommender systems, which works by recommending items that are similar to the items that a user has shown interest in, based on the content or attributes of those items. The algorithm creates a profile of the user’s preferences based on their interactions with those items, then looks for items with similar content or attributes to those previously interacted with and recommends them to the user. For example, if a user has shown a preference for action movies, the algorithm may recommend other action movies with similar themes, characters, or settings.

Hybrid Recommendation Systems

A hybrid recommendation system combines both collaborative filtering and content-based filtering to provide more accurate and diverse recommendations which help it overcome the limitations of individual methods and improve the overall performance of the recommender system.

User-User Algorithms

User-user algorithm recommends items based on the preferences of similar users which determines which users are similar. The algorithm looks at how many items they have in common in the dataset that is constantly growing the more users on a specific platform engage and interact. For example, if two users both followed similar people and liked similar content on a social media platform, they may be considered similar by the algorithm.

Item-Item Algorithms

A user-item recommendation algorithm is a type of algorithm used in recommender systems to suggest items to users based on their past behaviour or preferences. This algorithm works by analysing the behaviour of an individual user, such as their previous purchases, clicks, or ratings, and uses that information, which gets stored in a dataset, to recommend items that the user is likely to be interested in.

The Benefits of Recommendation Algorithms and How They Improve the User Experience

– Personalisation: One of the primary benefits of recommendation algorithms is personalisation. By analysing user behaviour and preferences, these algorithms can suggest products or content that is tailored to an individual user’s interests and needs. This personalised approach can lead to increased engagement and satisfaction, as users are more likely to find items they are interested in and less likely to waste time searching for items they may not like.

– Increased Engagement: Personalisation through recommendation algorithms can also lead to increased engagement with a platform. When users receive personalised recommendations, they are more likely to spend time exploring the platform and discovering new items. This increased engagement can lead to higher user satisfaction and loyalty.

– Higher Sales and Conversion: Recommendation algorithms can also lead to higher sales and conversions for online retailers. When users receive personalised recommendations, they are more likely to purchase items that are tailored to their interests, leading to increased sales and conversions. In fact, a study by Barilliance found that personalised product recommendations lead to a 70% increase in add-to-cart rates.

– Less Time-Consuming: Recommendation algorithms can also save users time by providing personalised recommendations without the need for manual searching. By analysing user behaviour and preferences, these algorithms can quickly suggest items that are likely to be of interest to a user, reducing the time spent searching for products or content.

– Improved User Experience: By providing personalised recommendations and saving users time, recommendation algorithms can lead to a better overall user experience. Users are more likely to be satisfied with a platform when they receive recommendations that are tailored to their interests and can quickly find the items they are looking for.

– Enhanced Customer Loyalty: Personalisation and a better user experience can also lead to increased customer loyalty. When users are satisfied with a platform and receive recommendations that are tailored to their interests, they are more likely to continue using the platform and recommend it to others.

– Improved Data Insights: Recommendation algorithms can also provide valuable data insights for online platforms. By analysing user behaviour and preferences, these algorithms can provide information on which products or content are most popular and what types of items users are interested in. This data can be used to improve the platform’s offerings and tailor recommendations even further.

Are Your Customers Being Served? Get In Touch To Learn More

Recommendation algorithms are shaping the social media landscape and will continue to do so for the foreseeable future. Your brand, therefore, needs to be creating content that can be found by your target audiences in their feeds. These new algorithms are full of benefits for small to medium-sized businesses that no longer need to rely on building followers over a long period of time. All it takes is one piece of content to go viral and transform your social media presence.

To hear more about our award-winning campaigns get in touch with our sales team today by phone at 0161 327 2635 or email [email protected].