发明名称 SYSTEMS AND METHODS FOR CONTENT RESPONSE PREDICTION
摘要 Techniques for predicting a user response to content are described. According to various embodiments, a configuration file is accessed, where the configuration file includes a user-specification of raw data accessible via external data sources and raw data encoding rules. In some embodiments, the raw data includes raw member data associated with a particular member and raw content data associated with a particular content item. Thereafter, source modules encode the raw data from the external data sources into feature vectors, based on the raw data encoding rules. An assembler module assembles one or more of the feature vectors into an assembled feature vector, based on user-specified assembly rules included in the configuration file. A prediction module performs a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item.
申请公布号 US2015088788(A1) 申请公布日期 2015.03.26
申请号 US201414557152 申请日期 2014.12.01
申请人 LinkedIn Corporation 发明人 Traupman Jonathan David;Agarwal Deepak;Zhang Liang;Long Bo;Astier Frank Emmanuel
分类号 G06N5/04;G06N7/00;G06N99/00 主分类号 G06N5/04
代理机构 代理人
主权项 1. A method comprising: accessing a configuration file that includes a user specification of data accessible via external data sources and data encoding rules, the data including member data associated with a particular member and content data associated with a particular content item; encoding, using a plurality of source modules, the data from the external data sources into feature vectors, based on the data encoding rules; assembling, using an assembler module, one or more of the feature vectors into an assembled feature vector, based on user-specified assembly rules included in the configuration file; and performing, using a prediction module, a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item.
地址 Mountain View CA US