互联网行为特征库产品

该产品依据运营商的上网日志入库标准（DPI）来挖掘用户的上网行为特征。通过深度的挖掘与分析，它建立了互联网上网日志特征与标签之间的精确对应关系，进而构建了一个标签规则库。产品处理流程可概括为：四必得基于HTTP/HTTPS等互联网用户常用的超文本协议，运用先进的机器学习技术，深入挖掘和分析海量的网络数据，再基于运营商提供的DPI话单库对规则特征进行准确性校验（四必得不接触用户个体数据），最终建立精确的应用规则库和功能规则库。例如，对于点击进入携程APP签证频道的行为，可以依据特征规则“downlo.ctr.com/visa*”来与之对应，并利用运营商提供的DPI话单库（对应合同：广州四必得科技有限公司与**分公司大数据业务合作协议）进行校验，以此计算出使用应用或场景下用户点击、打开、浏览等行为的统计级数据（不涉及用户个体数据），四必得基于业务经验或对照数据，最终验证使用应用或场景标签规则的准确性。该产品广泛应用于互联网用户上网行为的标注领域，能够精确识别并区分每个用户的上网场景，涵盖APP使用、小程序访问以及具体页面浏览等多种行为。同时，它还支持投放模型的优化。利用正负样本数据及丰富的互联网标签库，该产品深入挖掘特征并进行二分类预测，基于这些训练出适用于广告等多种业务场景的新模型。此外，它还具备强大的监测功能，能够实时跟踪应用场景的变化以及规则特征的效用，为优化决策提供有力的数据支持。

This product mines users' internet browsing behavior characteristics based on the operator's internet log storage standard (DPI). Through in-depth mining and analysis, it establishes an accurate correspondence between internet log features and tags, thereby constructing a tag rule base. The product processing flow can be summarized as follows: Sibide relies on hypertext protocols commonly used by internet users such as HTTP/HTTPS, applies advanced machine learning technologies to deeply mine and analyze massive network data, then verifies the accuracy of rule features based on the DPI call detail record repository provided by the operator (Sibide does not access individual user data), and finally establishes an accurate application rule base and function rule base. For example, for the behavior of clicking into the visa channel of the Ctrip APP, it can be matched with the feature rule "downlo.ctr.com/visa*", and verified using the DPI call detail record repository provided by the operator (corresponding contract: Big Data Business Cooperation Agreement between Sibide Technology (Guangzhou) Co., Ltd. and ** Branch), thereby calculating aggregate-level data such as user clicks, openings, and browsing behaviors under the application or scenario (no individual user data is involved). Sibide finally verifies the accuracy of the application or scenario tag rules based on business experience or comparative data. This product is widely used in the field of internet user browsing behavior annotation, and can accurately identify and distinguish each user's browsing scenarios, covering various behaviors such as APP usage, mini-program access, and specific page browsing. Meanwhile, it also supports the optimization of delivery models. Using positive and negative sample data and a rich internet tag library, the product deeply mines features and performs binary classification predictions, based on which new models suitable for various business scenarios such as advertising are trained. In addition, it also has powerful monitoring functions, which can track changes in application scenarios and the effectiveness of rule features in real time, providing strong data support for optimization decisions.