research-article

Mobile game props recommendation for machine learning

Authors: Can Yang, Ping LiAuthors Info & Claims

Journal of Intelligent & Fuzzy Systems, Volume 44, Issue 3

Pages 4093 - 4102

https://doi.org/10.3233/JIFS-220703

Published: 01 January 2023 Publication History

Abstract

Mobile game providers benefit by selling virtual items in the game. Each event is described as an example in the player log data, and the player indicates the purchase status of the various game props as a plurality of tags, the game props recommendation question is abstractd into a multi-instance multi-label learning problem. On this basis, the fast multi-instance multi-label learning algorithm is designed for recommendation of mobile online game props, and semi-supervised learning is used to improve the recommendation performance. Off-line data sets and the online game experimental results of the actual online mobile phone show that the game props based on multi-instance multi-tagging learning technology brings a significant increase in game revenue.

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Cited By

Yu DWang XXiong YShen XWu RWang DZou ZXu G(2023)MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online GamesACM Transactions on Intelligent Systems and Technology10.1145/362624315:4(1-23)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3626243

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cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology Volume 44, Issue 3

2023

1997 pages

ISSN:1064-1246

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© 2023 – IOS Press. All rights reserved.

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IOS Press

Netherlands

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Published: 01 January 2023

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Cited By

Yu DWang XXiong YShen XWu RWang DZou ZXu G(2023)MHANER: A Multi-source Heterogeneous Graph Attention Network for Explainable Recommendation in Online GamesACM Transactions on Intelligent Systems and Technology10.1145/362624315:4(1-23)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3626243

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