Please wait while we enable your Account


Contacting Amazon Web Services
Deploying Cloud Servers, Storage, Transcoding & Database Servers
Deploying Global CDN
Deploying Firewall & Enabling Security Measures
Deploying the CMS & Admin Module
Deploying Website, Mobile & TV Apps framework
Creating your FTP account
Finishing up all the modules
Preparing for launch

Use of AI Recommendation in the Gaming industry

Ankit Jena Published on : 29 March 2022
AI Recommendation


Gaming industry is witnessing rapid growth recently and will continue to expand exponentially. It is expected by 2025 the industry may grow to 300 billion dollars. With ever-increasing players, companies operating in the gaming industry are doing their best to develop the products and improve customer engagement.  AI-powered recommendation systems have a huge impact on various gaming businesses.

When it comes to online gaming, players have a huge list of games to choose from. Gaming platforms include thousands of games and discovering a relevant game can be a challenging task for players. AI Recommendation helps players discover their favorite games. A good recommendation engine suggests games that a player finds interesting or relevant and they would not have otherwise discovered.


What is an AI Recommendation?

AI recommendation is a real-time suggestion given to the audience by using machine learning algorithms. It is the process of predicting the user’s choice and offering relevant suggestions. With the usage of data science along with the user’s data, the recommender system can recommend the most suitable items to a particular user. Recommendations done by a smart recommender system are somehow similar to the activities of an experienced shop assistant who exactly knows the needs, choices, and requirements of the consumer by analyzing their behavior.


AI Recommendation


3 Common Methods used for game recommendations

There are three common types of methods used to generate recommendations:

  • Content-based filtering
  • Collaborative filtering
  • Hybrid filtering


Content-based filtering

It uses only information about an item to recommend related relevant items. This is accomplished by extracting information from metadata about the item, such as game genre, other item tags. It usually recommends items within the same sub-genre.

One of the major benefits of content-based filtering is that it doesn’t need any prior data about the user to make recommendations. Content-based filtering never goes through a cold start problem because it does not use users’ data.


Collaborative filtering

This type of filtering uses data collected from end-users and transactions to find out which content they will find interesting. It makes recommendations for you based on the preferences of other users, rather than the genres of the games that you have played before.

One of the great advantages of collaborative filtering is that it can be used to diminish the number of metadata that is required to be maintained about items in the catalog. It can be used for both item ratings as well as rankings. In an item rating system, a recommender system predicts how a user rates a game that is not yet rated. In an item ranking system, the recommender system suggests content that the user is most likely to interact with next.

Netflix is a great example of an item rating system. It predicts how users will rate a new movie. Amazon is an instance of an item ranking system. It predicts related items but doesn’t show explicit scores.


Hybrid filtering

Hybrid filtering is a special kind of recommender system that offers suggestions to the user by combining two methods i.e., the content-based as well as collaborative filtering method. It helps overcome the challenges faced by utilizing them separately. It helps industries in segregating and generating helpful information from the bulk of data accessible. Combining the best of both helps gaming businesses provide a personalized experience to the end-users.


Using a Recommender System 

A Game recommendation system suggests games based on users’ likes and dislikes. It helps generate high-quality recommendations for the users. A sound recommender system works effectively according to the game’s data. The ever-expanding gaming industry can truly benefit from the recommender system that would help in presenting the right games in front of the audience and would also be economically beneficial to the industry.


Alie – Advanced Recommendation Engine for Games

Alie is an AI-powered recommendation engine that facilitates a personalized user experience across websites and applications. It provides real-time recommendations to users. Its exclusive machine learning algorithm is specifically designed to analyze user data and recommend personalized content. Start a 14-days free trial to know how Alie can help your gaming platform.


AI Recommendation

Written by: Ankit Jena

Ankit is Content Writer for Muvi’s Marketing unit. He is a passionate writer with 5+ Years of Experience in Content Creation And Development. In his past time, he likes to dance, play football and google various things to quench his thirst for knowledge.

Add your comment

Leave a Reply

Your email address will not be published.

Try Alie free for 14 days

No Credit Card Required

Upcoming Webinar
May 30

9:00AM PST

API Security in Video Streaming

While video streaming, we have to deal with a lot of data which is sensitive and confidential related mostly to users and content. In order to keep that…...

Event Language: English
30 Minutes