Method and apparatus for training recommendation model, computer device, and storage medium

An apparatus for training a recommendation model, the apparatus comprising:

• one or more processors and one or more memories, the one or more memories storing at least one piece of program code, the at least one piece of program code, when being loaded and executed by the one or more processors, implementing:
• acquiring first preference information corresponding to a plurality of users, the first preference information representing preference degrees of the users for a first object, the first object being an object on which the users have performed a designated operation;
• determining second preference information corresponding to the plurality of users based on similarities between the first preference information corresponding to the plurality of users, the second preference information representing preference degrees of the users for a second object, the second object being an object on which the users have not performed the designated operation, comprising:
• respectively constructing preference vectors corresponding to the users based on the first preference information of the users for the first object;
• respectively acquiring hash values of the preference vectors corresponding to the users;
• grouping the users based on the preference vectors corresponding to the users, and categorizing at least two users into one group in response to that the hash values of the preference vectors corresponding to the at least two users are the same, wherein a user quantity of users included in each group is restricted so as to prevent an amount of data in a single group from exceeding a kernel memory of a server;
• and
• determining, for one user, the second preference information of the user for the second object based on the first preference information corresponding to users belonging to the same group as the user;
• correcting the first preference information and the second preference information based on preference features of users for an object and correlation features between objects and the users, to obtain third preference information of the users for the objects, the preference features and the correlation features being determined based on preference degrees of the users for the objects;
• generating, by a computing engine, training data based on the third preference information, a user attribute of a target user to which the third preference information belongs, an object attribute of a target object to which the third preference information belongs, and operation information of the target user for the target object, wherein before generating the training data, a storage type of a string format data is converted into a number format, so as to reduce memory consumption;
• and
• performing iterative training on a target recommendation model based on the training data, and obtaining a trained target recommendation model in response to that an output result of the target recommendation model satisfies a training condition.

A non-transitory computer-readable storage medium, the computer-readable storage medium storing at least one piece of program code, the at least one piece of program code, when being loaded and executed by a processor, implementing:

• acquiring first preference information corresponding to a plurality of users, the first preference information representing preference degrees of the users for a first object, the first object being an object on which the users have performed a designated operation;
• determining second preference information corresponding to the plurality of users based on similarities between the first preference information corresponding to the plurality of users, the second preference information representing preference degrees of the users for a second object, the second object being an object on which the users have not performed the designated operation, comprising:
• respectively constructing preference vectors corresponding to the users based on the first preference information of the users for the first object;
• respectively acquiring hash values of the preference vectors corresponding to the users;
• grouping the users based on the preference vectors corresponding to the users, and categorizing at least two users into one group in response to that the hash values of the preference vectors corresponding to the at least two users are the same, wherein a user quantity of users included in each group is restricted so as to prevent an amount of data in a single group from exceeding a kernel memory of the server;
• and
• determining, for one user, the second preference information of the user for the second object based on the first preference information corresponding to users belonging to the same group as the user;
• correcting the first preference information and the second preference information based on preference features of users for an object and correlation features between objects and the users, to obtain third preference information of the users for the objects, the preference features and the correlation features being determined based on preference degrees of the users for the objects;
• generating, by a computing engine, training data based on the third preference information, a user attribute of a target user to which the third preference information belongs, an object attribute of a target object to which the third preference information belongs, and operation information of the target user for the target object, wherein before generating the training data, a storage type of a string format data is converted into a number format, so as to reduce memory consumption;
• and
• performing iterative training on a target recommendation model based on the training data, and obtaining a trained target recommendation model in response to that an output result of the target recommendation model satisfies a training condition.