The aim of the AIDock game recommendation system is to give suggestions to any player on games that the player has not played, but may be interested in. Playing games is a recurrent activity and a preferred game is played many times, but players also want to discover new games. The game recommender system can suggest players a more direct route to the games when they have exhausted a game or when they are exploring, thereby improving their purchasing and general in-game experience. This may ultimately result in increased revenue for your company.
Some examples of recommender systems which the average consumer will be familiar with are the likes of product recommendations on online shopping sites after particular google searches are made, suggestions for movies and TV shows on streaming services based on what you have been watching up to that point and recommended videos on social media and music streaming sites. AI game recommendation systems and the requisite game recommendation engine are part of a family of consumer tactics that are so commonplace now that you are probably experiencing them all the time and don’t even understand that it’s happening. We want to make our experience for game players as smooth as that of Netflix or YouTube, so that they continue to engage with your games and return to your site.
A user dataset will be utilised to analyse the patterns of each player. It will contain different important metrics such as the user id, the game title, the behavior they are exhibiting while playing and ultimately what the recommendation may be for the player after the analysis of that data. Each row of the dataset will represent a specific behavior of a user towards a game, such as how long they play, if they tend to quit having lost early or if they are more interested in the sunk cost approach to a game. If the behavior is to keep playing despite a big win or loss, the value associated with it will likely correspond to the amount of time with which the player has been engaging. If the behavior is to quit after a certain point, the computer will analyse what that point is and will attempt to keep this player coming back to your games.
Users’ behavior data is vital documented information that tells you what you need to know about the engagement of the user on the game, which can be collected through a variety of methods and studies of patterns. It is used in conjunction with User demographic information. This brand of data is related to the user’s personal information which they have volunteered to the site and signed terms and conditions forms to allow you to use for this and other purposes. They will examine these datasets along with product attribute data, which is information related to the product itself such as the type of game the user is playing.
There will also be a certain degree of user similarity employed, where our technology will cross reference the behaviors of certain users to get a general idea of what to recommend to slightly newer players whose data we don’t have the full information on as of yet. This will engage people early, and keep players on the site and playing your games, which is the most important.
People are also likely to spread the word of your website if they find a game they weren’t expecting to enjoy thanks to your algorithms. AIDock will often create an environment where players want to stick around on the website for hours, which is great for your bottom line. We have a number of satisfied clients who will swear by the methods that we implement, such as the game recommendation engine, which is constantly developing as technology and gaming patterns begin to change.