Recommendation method, method for training recommendation model, and related product

A recommendation device comprising:

• a memory storing executable instructions;
• and
• a processor configured to execute the executable instructions to perform operations of:
• obtaining to-be-predicted data;
• obtaining a plurality of target reference samples from a plurality of reference samples based on a similarity between the to-be-predicted data and the plurality of reference samples, wherein each reference sample and the to-be-predicted data each comprise user feature field data and item feature field data, the user feature field data of the to-be-predicted data indicates a feature of a target user, the item feature field data of the to-be-predicted data indicates a feature of a target item, and each target reference sample and the to-be-predicted data have partially identical user feature field data and/or item feature field data;
• obtaining target feature information of the to-be-predicted data based on the plurality of target reference samples and the to-be-predicted data, wherein the target feature information comprises a first target eigenvector group and a second target eigenvector group, the first target eigenvector group is vectorized to-be-predicted data, and the second target eigenvector group is obtained by vectorizing the plurality of target reference samples and then performing fusion on vectorized target reference samples;
• obtaining an output value through a deep neural network (DNN) by using the target feature information as input;
• and
• determining, based on the output value, whether to recommend the target item to the target user.

A non-transitory computer-readable storage medium having stored thereon executable instructions that, when executed by a processor of a recommendation device, cause the recommendation device to perform operations of:

• obtaining to-be-predicted data;
• obtaining a plurality of target reference samples from a plurality of reference samples based on a similarity between the to-be-predicted data and the plurality of reference samples, wherein each reference sample and the to-be-predicted data each comprise user feature field data and item feature field data, the user feature field data of the to-be-predicted data indicates a feature of a target user, the item feature field data of the to-be-predicted data indicates a feature of a target item, and each target reference sample and the to-be-predicted data have partially identical user feature field data and/or item feature field data;
• obtaining target feature information of the to-be-predicted data based on the plurality of target reference samples and the to-be-predicted data, wherein the target feature information comprises a first target eigenvector group and a second target eigenvector group, the first target eigenvector group is vectorized to-be-predicted data, and the second target eigenvector group is obtained by vectorizing the plurality of target reference samples and then performing fusion on vectorized target reference samples;
• obtaining an output value through a deep neural network (DNN) by using the target feature information as input;
• and
• determining, based on the output value, whether to recommend the target item to the target user.