Information processing

An electronic device, comprising:

• at least one processor;
• and
• at least one memory, the at least one memory being coupled to the at least one processor and storing instructions executable by the at least one processor, the instructions, when executed by the at least one processor, causing the electronic device to perform acts comprising:
• determining, in response to there being a user sequence associated with a recommendation object, an input feature sequence for a machine learning model based on at least one object feature representation of the recommendation object and at least one first user feature representation corresponding to at least one user in the user sequence, wherein the user sequence is determined based on users who perform interactions with the recommendation object;
• generating, based on the input feature sequence, a predicted user feature representation for the recommendation object using the machine learning model, wherein the predicted user feature representation is used to indicate a user who performs an interaction with the recommendation object at a future time;
• extracting, in response to receiving a recommendation request, a second user feature representation corresponding to an initiating user of the recommendation request;
• and
• providing, in response to a similarity between the second user feature representation and the predicted user feature representation satisfying a similarity requirement, the recommendation object to the initiating user of the recommendation request.

A non-transitory computer-readable storage medium having computer-executable instructions stored thereon, the computer-executable instructions being executable by a processor to perform acts comprising:

• determining, in response to there being a user sequence associated with a recommendation object, an input feature sequence for a machine learning model based on at least one object feature representation of the recommendation object and at least one first user feature representation corresponding to at least one user in the user sequence, wherein the user sequence is determined based on users who perform interactions with the recommendation object;
• generating, based on the input feature sequence, a predicted user feature representation for the recommendation object using the machine learning model, wherein the predicted user feature representation is used to indicate a user who performs an interaction with the recommendation object at a future time;
• extracting, in response to receiving a recommendation request, a second user feature representation corresponding to an initiating user of the recommendation request;
• and
• providing, in response to a similarity between the second user feature representation and the predicted user feature representation satisfying a similarity requirement, the recommendation object to the initiating user of the recommendation request.