Method and apparatus for constructing recommendation model and neural network model, electronic device, and storage medium

An electronic device, comprising:

• a memory, configured to store executable instructions;
• and
• a processor, configured to perform, when executing the executable instructions stored in the memory, a method for constructing a recommendation model, the method including:
• aggregating a plurality of feature tables corresponding to each application scenario in a recommendation project to obtain an aggregated feature table, the recommendation project comprising a plurality of application scenarios in a one-to-one correspondence with a plurality of recommendation indicators of an information item and each application scenario having a recommendation model being configured to predict a corresponding recommendation indicator;
• receiving a corresponding user feature and item feature from the aggregated feature table based on a user identifier and an item identifier comprised in a sample data table, and stitching the user feature and the item feature with the sample data table, to form a training sample set;
• performing primary key encoding on a feature identifier of each training sample of the training sample set, to obtain a primary key encoded value of the feature identifier;
• performing secondary key encoding on the feature identifier of each training sample, to obtain a secondary key encoded value of the feature identifier;
• stitching the primary key encoded value and the secondary key encoded value, to obtain an index encoded value of the feature identifier;
• updating the feature identifier of the training sample to the index encoded value, to obtain an updated training sample set;
• training the recommendation model of the application scenario based on the updated training sample set, the trained recommendation model being capable of fitting a user feature and an item feature in the updated training sample set;
• and
• recommending information items to at least one user using the trained recommendation model.

A non-transitory computer-readable storage medium, storing executable instructions that, when executed by a processor of a computer device, implements a method for constructing a recommendation model, the method including:

• aggregating a plurality of feature tables corresponding to each application scenario in a recommendation project to obtain an aggregated feature table, the recommendation project comprising a plurality of application scenarios in a one-to-one correspondence with a plurality of recommendation indicators of an information item and each application scenario having a recommendation model being configured to predict a corresponding recommendation indicator;
• receiving a corresponding user feature and item feature from the aggregated feature table based on a user identifier and an item identifier comprised in a sample data table, and stitching the user feature and the item feature with the sample data table, to form a training sample set;
• performing primary key encoding on a feature identifier of each training sample of the training sample set, to obtain a primary key encoded value of the feature identifier;
• performing secondary key encoding on the feature identifier of each training sample, to obtain a secondary key encoded value of the feature identifier;
• stitching the primary key encoded value and the secondary key encoded value, to obtain an index encoded value of the feature identifier;
• updating the feature identifier of the training sample to the index encoded value, to obtain an updated training sample set;
• training the recommendation model of the application scenario based on the updated training sample set, the trained recommendation model being capable of fitting a user feature and an item feature in the updated training sample set;
• and
• recommending information items to at least one user using the trained recommendation model.