关键词: 机器学习, 多示例多标记学习（MIML）, 半监督学习, 推荐

Abstract: Mobile phone game providers gain profits by selling virtual props in games. This paper describes an event in game log data of players as an instance and represents the states of players’ prop purchases by labels, so that the game props recommendation is modeled as multi-instance multi-label learning (MIML) problem. On the basis of this abstraction, an MIMLfast algorithm is used to recommend mobile phone game props and a semi-supervised learning part is integrated to improve the performance of recommendation. The results of experiments conducted on offline data sets and a real mobile phone game show that the MIML-based game props recommendation brings a remarkable increase of game profits.

Key words: machine learning, multi-instance multi-label learning (MIML), semi-supervised learning, recommendation