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Title: GitHub - IronmanJay/UserBehaviorAnalysis: 模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类是能够明显地表现出用户兴趣的行为，比如对商品的收藏、喜欢、评分和评价，对数据进行深入分析，得到用户画像，进而对用户给出个性化的推荐商品列表；另一类则是常规的业务操作，关注异常状况以做好风控，比如登录和订单支付。

Open Graph Title: GitHub - IronmanJay/UserBehaviorAnalysis: 模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类是能够明显地表现出用户兴趣的行为，比如对商品的收藏、喜欢、评分和评价，对数据进行深入分析，得到用户画像，进而对用户给出个性化的推荐商品列表；另一类则是常规的业务操作，关注异常状况以做好风控，比如登录和订单支付。

X Title: GitHub - IronmanJay/UserBehaviorAnalysis: 模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类是能够明显地表现出用户兴趣的行为，比如对商品的收藏、喜欢、评分和评价，对数据进行深入分析，得到用户画像，进而对用户给出个性化的推荐商品列表；另一类则是常规的业务操作，关注异常状况以做好风控，比如登录和订单支付。

Description: 模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类是能够明显地表现出用户兴趣的行为，比如对商品的收藏、喜欢、评分和评价，对数据进行深入分析，得到用户画像，进而对用户给出个性化的推荐商品列表；另一类则是常规的业务操作，关注异常状况以做好风控，比如登录和订单支付。 - GitHub - IronmanJay/UserBehaviorAnalysis: 模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类是能够明显地表现出用户兴趣的行为，比如对商品的收藏、喜欢、评分和评价，对数据进行深入分析，得到用户画像，进而对用户给出个性化的推荐商品列表；另一类则是常规的业务操作，关注异常状况以做好风控，比如登录和订单支付。

Open Graph Description: 模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类...

X Description: 模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类...

Opengraph URL: https://github.com/IronmanJay/UserBehaviorAnalysis

X: @github

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og:image:alt模拟电商系统上线运行一段时间后，根据收集到大量的用户行为数据，利用大数据技术（Flink）进行深入挖掘和分析，进而得到感兴趣的商业指标并增强对风险的控制。 整体可以分为用户行为习惯数据和业务行为数据两大类。用户的行为习惯数据包括了用户的登录方式、上线的时间点及时长、点击和浏览页面、页面停留时间以及页面跳转等等，从中进行流量统计和热门商品的统计，并深入挖掘用户的特征；业务行为数据分为两类：一类...
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