Method and apparatus for training online prediction model, device and storage medium

An electronic device, comprising:

• one or more processors;
• and
• a storage apparatus storing one or more programs,
• wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform operations, the one or more programs comprising instructions which instruct the one or more processors to perform the operations, and the instructions comprising:
• an instruction to acquire an offline sample feature and an online sample feature of a user, the offline sample feature comprising a user portrait feature, and the instruction to acquire the online sample feature comprising an instruction to determine the online sample feature of the user based on a user online real-time behavior, wherein the user online real-time behavior comprises a user online operation behavior;
• an instruction to offline train a Wide&Deep model based on both the offline sample feature and the online sample feature of the user, to obtain an offline recommendation model, wherein the instruction to offline train the Wide&Deep model comprises:
• an instruction to input the offline sample feature of the user into a Deep layer of the Wide&Deep model to generate a Deep layer feature, wherein the Deep layer is a deep neural network;
• an instruction to input the online sample feature after feature engineering into a Wide layer of the Wide&Deep model to generate a Wide layer feature, wherein the Wide layer is a wide linear model;
• and
• an instruction to splice the Wide layer feature and the Deep layer feature to obtain a spliced feature and input the spliced feature into a logic regression for prediction training, to obtain a trained offline recommendation model;
• an instruction to acquire a latest online feature of the user, and online train the Wide&Deep model based on the latest online feature of the user to obtain an online learning model by:
• adjusting a Wide layer parameter of the Wide&Deep model, so that the latest online feature is adapted for use as the online sample feature to be input into the Wide layer, the online learning model being used to adapt the latest online feature for use as an online sample feature to be input into the trained offline recommendation model,
• an instruction to convert the offline recommendation model to an online recommendation model, and input the latest online feature output by the online learning model into the online recommendation model to generate an online prediction model;
• and
• an instruction to determine a recommended ranking or a click-through rate estimation in a recommendation system, based on an output of the online prediction model.

A non-transitory computer readable storage medium, storing a computer program thereon, wherein the program, when executed by a processor, cause the processor to perform operations, the one or more programs comprising instructions which instruct the one or more processors to perform the operations, and the instructions comprising:

• an instruction to acquire an offline sample feature and an online sample feature of a user, the offline sample feature comprising a user portrait feature, the acquiring the online sample feature comprising determining the online sample feature of the user based on a user online real-time behavior, wherein the user online real-time behavior comprises a user online operation behavior;
• an instruction to offline train a Wide&Deep model based on both the offline sample feature and the online sample feature of the user, to obtain an offline recommendation model, wherein the instruction to offline train the Wide&Deep model comprises:
• an instruction to input the offline sample feature of the user into a Deep layer of the Wide&Deep model to generate a Deep layer feature, wherein the Deep layer is a deep neural network;
• an instruction to input the online sample feature after feature engineering into a Wide layer of the Wide&Deep model to generate a Wide layer feature, wherein the Wide layer is a wide linear model;
• and
• an instruction to splice the Wide layer feature and the Deep layer feature to obtain a spliced feature and input the spliced feature into a logic regression for prediction training, to obtain a trained offline recommendation model;
• an instruction to acquire a latest online feature of the user, and online training the Wide&Deep model based on the latest online feature of the user to obtain an online learning model by:
• adjusting a Wide layer parameter of the Wide&Deep model, so that the latest online feature is adapted for use as the online sample feature to be input into the Wide layer, the online learning model being used to adapt the latest online feature for use as an online sample feature to be input into the trained offline recommendation model;
• an instruction to convert the offline recommendation model to an online recommendation model, and inputting the latest online feature output by the online learning model into the online recommendation model to generate an online prediction model;
• and
• an instruction to determine a recommended ranking or a click-through rate estimation in a recommendation system, based on an output of the online prediction model.