Methods for generating recommendations based on user preferences and video game characteristics

The research is devoted to studying the video game recommendation process based on the consideration of user preferences and video game characteristics to improve the efficiency of selection by implementing intelligent recommendation generation algorithms.While there are many recommendation algorithms and techniques, most of them fall into the following broad categories: collaborative filtering, content filtering, and contextual filtering.Collaborative filtering algorithms recommend items based on information about the preferences of many users, and build a model based on a user's past behavior, such as items previously purchased or ratings given to those items, as well as similar decisions by other users.Content filtering uses attributes or features of an object to recommend other objects that are similar to the user's preferences.It is based on the similarity of the characteristics of the item and the user, taking into account information about the user and the items he or she has interacted with, and models the probability of a new interaction.Hybrid recommender systems combine the advantages of the above types to create a more comprehensive recommender system.Contextual filtering uses the sequence of contextual user actions to predict the likelihood of the next action.In this article, three different recommender system algorithms, one content-based and two collaborative filtering algorithms (one with the ALS algorithm and the other with the EM and SVD algorithms), were proposed and studied to generate video game selection suggestions.To test the effectiveness of the algorithms, we used datasets of users and video game characteristics available on Kaggle.The recommendation system with the ALS algorithm provides the best recommendations based on the evaluation, so it was implemented in the prototype.The recommender application can significantly improve users' gaming experience, making the process of choosing games more personalized and efficient.