-
公开(公告)号：US10678997B2
公开(公告)日：2020-06-09
申请号：US15825657
申请日：2017-11-29
发明人： Karan Ashok Ahuja , Befekadu Ayenew Ejigou , Ningfeng Liang , Lokesh P. Bajaj , Wei Wang , Paul Fletcher , Wei Lu , Shaunak Chatterjee , Souvik Ghosh , Yang Li , Wei Deng , Qiang Wu
IPC分类号： G06F17/00 , G06F40/174 , G06Q50/00 , G06N20/00 , H04L29/08
摘要： In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.
-
公开(公告)号：US20190108209A1
公开(公告)日：2019-04-11
申请号：US15825657
申请日：2017-11-29
发明人： Karan Ashok Ahuja , Befekadu Ayenew Ejigou , Ningfeng Liang , Lokesh P. Bajaj , Wei Wang , Paul Fletcher , Wei Lu , Shaunak Chatterjee , Souvik Ghosh , Yang Li , Wei Deng , Qiang Wu
摘要： In an example, first and second machine learned models corresponding to a particular context of a social networking service are obtained, the first machine learned model trained via a first machine learning algorithm to output an indication of importance of a social networking profile field to obtaining results in the particular context, and the second machine learned model trained via a second machine learning algorithm to output a propensity of the user to edit a social networking profile field if requested. One or more missing fields in a social networking profile for the user are identified. For each of one or more of the one or more missing fields, the field and an identification of the user are passed through the first and second machine learned models, and outputs of the first and second machine learned models are combined to identify one or more top missing profile fields.
-
搜索结果
2 条记录 (耗时 0.012 秒)
