{"title":"使用神经网络找到游戏内购买的关键因素","authors":"Long-Sheng Chen, Meng-Ru Lin, Yi-Ting Pan","doi":"10.1109/ICAWST.2017.8256473","DOIUrl":null,"url":null,"abstract":"According to the several forecasts, mobile games industry will account for ever increasing contributions, with a market share approaching 35% by 2020. Consequently, the game applications (App) providers need to know how to design products that match consumer's requirements, continuously use, and in-app purchase are important issue. In particular, in-App purchase is the major revenue models. Hence, this study attempts to define the potential factors of influencing in-App purchases for game users. Then, we use two feature selection methods, Neural Network Pruning and Chi-square test to identify important factors that affect users' in-game purchases behaviors. The results can be used as a reference when designing game Apps for game developers and researchers.","PeriodicalId":378618,"journal":{"name":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Find crucial factors of in-game purchase using neural networks\",\"authors\":\"Long-Sheng Chen, Meng-Ru Lin, Yi-Ting Pan\",\"doi\":\"10.1109/ICAWST.2017.8256473\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"According to the several forecasts, mobile games industry will account for ever increasing contributions, with a market share approaching 35% by 2020. Consequently, the game applications (App) providers need to know how to design products that match consumer's requirements, continuously use, and in-app purchase are important issue. In particular, in-App purchase is the major revenue models. Hence, this study attempts to define the potential factors of influencing in-App purchases for game users. Then, we use two feature selection methods, Neural Network Pruning and Chi-square test to identify important factors that affect users' in-game purchases behaviors. The results can be used as a reference when designing game Apps for game developers and researchers.\",\"PeriodicalId\":378618,\"journal\":{\"name\":\"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAWST.2017.8256473\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAWST.2017.8256473","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Find crucial factors of in-game purchase using neural networks
According to the several forecasts, mobile games industry will account for ever increasing contributions, with a market share approaching 35% by 2020. Consequently, the game applications (App) providers need to know how to design products that match consumer's requirements, continuously use, and in-app purchase are important issue. In particular, in-App purchase is the major revenue models. Hence, this study attempts to define the potential factors of influencing in-App purchases for game users. Then, we use two feature selection methods, Neural Network Pruning and Chi-square test to identify important factors that affect users' in-game purchases behaviors. The results can be used as a reference when designing game Apps for game developers and researchers.