Mobile game props recommendation for machine learning

Article type: Research Article

Authors: Yang, Can^{; *} | Li, Ping

Affiliations: Hunan Institute of Traffic Engineering, Hengyang, China

Correspondence: [*] Corresponding author. Can Yang, Hunan Institute of Traffic Engineering, Hengyang, China. E-mail: 883344210@qq.com.

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.

Keywords: Machine learning, Multi-Instance Multi-Label Learning (MIML), semi-supervised learning, recommendation

DOI: 10.3233/JIFS-220703

Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4093-4102, 2023