A multi-criteria recommender system for NFT based IAP in RPG game

Pradana, Reza Putra, Hariadi, Mochamad, Rachmadi, Reza Fuad and Arif, Yunifa Miftachul (2022) A multi-criteria recommender system for NFT based IAP in RPG game. Presented at 2022 International Seminar on Intelligent Technology and Its Applications (ISITIA). (Submitted)

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Abstract

The market value of the gaming industry in 2021 is said to be more than 198.40 billion USD. Market value is also supported by the number of gamers, with 4.75 billion in January 2022. In today's In-App Purchase (IAP) income method has become a big trend in today's modern era models of free-to-play games, where gamers can choose or buy many items during the game to speed up the progress of the game or enjoy the full content of the game. However, sometimes players are overwhelmed with the number of items on offer, making it difficult for players to choose because the game content is too diverse. In addition, players are also worried about the security of their digital assets because, in 2021, there will be 7.5 million digital assets lost due to hacking. We propose a recommendation system to make it easier for players to choose items that suit their playstyle to solve this problem. We use the multi-criteria recommender system (MCRS) method because this method can improve the accuracy of recommendations compared to conventional recommendations that only use one criterion. In this study, we used eight criteria to calculate the recommendations. The results of our recommendation test show the accuracy value = 0.71, precision = 0.76, recall = 0.71 and F1 score = 0.66. To address security issues, we propose the implementation of a Non-Fungible Token (NFT) for each item. NFT can increase security because it uses a decentralized blockchain architecture in which every transaction is encrypted. The system guarantees that the assets will remain online so that users do not risk losing ownership of their assets when the developer changes game data, or the game server closes.

Item Type:	Conference (Paper)
Keywords:	Game Industry; In-App Purchase; Multi-Criteria Recommender System; Non-Fungible Token; Blockchain
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080101 Adaptive Agents and Intelligent Robotics 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080111 Virtual Reality and Related Simulation
Depositing User:	Yunifa Miftachul Arif
Date Deposited:	20 Nov 2022 23:28

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