Abstract: A multimedia content pushing method and an apparatus, a computer device, and a storage medium are provided. The method includes: obtaining historical operation information of alternative users on a target multimedia content in a target historical time period and real-time operation information on the target multimedia content; determining a target sample user from the alternative users based on the historical operation information and the real-time operation information, and training a neural network model based on sample data composed of sample attribute information corresponding to the target sample user and multimedia attribute information of the target multimedia content to obtain a target neural network model, and in response to a target user of the first type triggering a preset push event, using the target neural network model to determine a push strategy, and pushing the target multimedia content based on the push strategy.

Abstract: The disclosure relates to a method for information recommendation, an apparatus, an electronic device, and a storage medium. The method includes: determining a historical interaction parameter of a target user for associated information of each candidate object in a candidate object set; determining a target interaction parameter corresponding to each candidate object; ranking the candidate objects in the candidate object set based on the historical interaction parameter of the target user for the associated information of each candidate object and the target interaction parameter corresponding to each candidate object, to obtain a ranking result; and recommending, based on the ranking result, the associated information of the candidate objects to the target user.

Abstract: A multimedia content pushing method and an apparatus, a computer device, and a storage medium are provided. The method includes: obtaining historical operation information of alternative users on a target multimedia content in a target historical time period and real-time operation information on the target multimedia content; determining a target sample user from the alternative users based on the historical operation information and the real-time operation information, and training a neural network model based on sample data composed of sample attribute information corresponding to the target sample user and multimedia attribute information of the target multimedia content to obtain a target neural network model, and in response to a target user of the first type triggering a preset push event, using the target neural network model to determine a push strategy, and pushing the target multimedia content based on the push strategy.