An Area Recommendation Method Using Similarity Analysis for Play Patterns in MMORPG

Abstract

Recently, game companies have been increasingly offering a variety of content in their games. The more this happens, the more players will need to consider what is best for them. Players who have played such a game may not find it difficult to play, but those who are not used to play may have a hard time finding content. Therefore, in this paper, we try to give a customized guide to players in Massively Multiplayer Online Role-Playing Games (MMORPGs). We compare the similarity of growth speeds and visited areas, and then utilize this information to recommend the most similar characters. In this work, the K-means algorithm is used for clustering based on location, the Euclidean distance is calculated to recommend similar characters with similar growth speeds. In addition, Jaccard Similarity is introduced to recommend similar characters with similar access areas. Finally, we propose a method to recommend suitable areas by applying the access speed to the recommended characters in the previous steps. Our method achieves Precision and Recall of 0.74 and 0.81, respectively, on the real-life PvE (Player VS Environment) dataset.

Keywords

• Euclidean distance
• Jaccard similarity
• K-means clustering
• MMORPG
• game
• recommendation method