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

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...

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Bibliographic Details
Published in:Applied sciences 2022-11, Vol.12 (21), p.10833
Main Authors: Jo, Yuyeon, Cui, Shengmin, Joe, Inwhee
Format: Article
Language:English
Subjects:
Clustering
Computer & video games
Design
Euclidean distance
Euclidean geometry
game
Games
Information processing
Jaccard similarity
K-means clustering
MMORPG
Multimedia
Players
recommendation method
Recommender systems
Role playing
Semantics
Similarity
Citations: Items that this one cites
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app122110833