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ARTIFICIAL INTELLIGENCE AND/OR MACHINE LEARNING MODELS TRAINED TO PREDICT USER ACTIONS BASED ON AN EMBEDDING OF NETWORK LOCATIONS
ARTIFICIAL INTELLIGENCE AND/OR MACHINE LEARNING MODELS TRAINED TO PREDICT USER ACTIONS BASED ON AN EMBEDDING OF NETWORK LOCATIONS
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机译:人工智能和/或机器学习模型培训,以基于网络位置的嵌入来预测用户动作
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摘要
A computer-implemented method can facilitate delivery of targeted content to user devices in situations in which historic tracking data (e.g., cookie data) is generally unavailable and/or unreliable. A p-dimensional embedding of websites can be generated based on a group of user devices for whom tracking data is available. Conversion event data that indicates indicating whether that audience member performed a conversion action can be received. A machine learning model can be trained using the conversion event data and the positions of websites appearing in the conversion event data within the p-dimensional embedding to predict a likelihood of conversion and/or a type of content to provide given a position in the p-dimensional embedding. When an indication that a user device is accessing a website is received, a position of that website in the p-dimensional embedding can be determined and targeted content can be delivered to the user device.
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