应用商店推荐引擎优化

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Selina Xue

文档探讨了应用商店推荐的现状，指出当前推荐主要依赖下载量和评分的粗放方式，无法满足用户个性化需求。为了改善推荐效果，建议采用更精细化的方法，融入用户行为数据分析，如留存率和活跃度等。最终目标是实现个性化推荐，使用户找到更符合其喜好的应用。

Technology

超过 500,000 Apps

超过 450,000 Apps

超过 220,000 Apps

超过 100,000 Apps

Sheldon Recommend Star Trek to
happy

what the hell

向不同的用户推荐相同的东西

doubt it

1. 用户留存率 / 卸载率

最近7天，14天，30天，应用有效安装（用户或者设备数）与总安装（用户或者设备数）比例

用户下载、打开、卸载应用的时间戳

3. 用户安装应用列表

安装应用的类型分布，判断用户喜欢哪些应用

安装应用的趋势，判断用户最近喜欢哪些应用

新浪微博：@iamshooting
博客：http://paradise4ever.com

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