A Mapping Approach to Identify Player Types for Game Recommendations

Yeonghun Lee,Yuchul Jung

doi:10.3390/info10120379

Abstract

As the size of the domestic and international gaming industry gradually grows, various games are undergoing rapid development cycles to compete in the current market. However, selecting and recommending suitable games for users continues to be a challenging problem. Although game recommendation systems based on the prior gaming experience of users exist, they are limited owing to the cold start problem. Unlike existing approaches, the current study addressed existing problems by identifying the personality of the user through a personality diagnostic test and mapping the personality to the player type. In addition, an Android app-based prototype was developed that recommends games by mapping tag information about the user’s personality and the game. A set of user experiments were conducted to verify the feasibility of the proposed mapping model and the recommendation prototype.

Highlights

In modern society, gaming has become a part of popular culture that everyone enjoys regardless of gender or age
Game Recommendation Based on the Identified Player Type
In order to overcome the problem of cold start, which is a disadvantage in existing game recommendation systems, the present study has proposed an OCEAN player type mapping by only with related tags

Summary

Introduction

In modern society, gaming has become a part of popular culture that everyone enjoys regardless of gender or age. A new genre that combines several existing game genres has been researched and developed to meet the needs of users. Owing to such efforts, the gaming industry has grown and will continue to expand steadily. Statistics from a platform called Steam, which is one of the leading overseas online game platforms, show that a total of 9050 games have been released in the year 2018. This shows a 28% increase over the year

Results

Discussion

Conclusion