Media file recommendations for a search engine

A non-transitory computer-readable medium storing a program, which when executed by a computer, configures the computer to:

• determine a style preference score for a new media file based on a recommendation model, wherein the new media file is selected by a user, wherein the recommendation model is a machine learning model using a neural network;
• and
• when the style preference score is lower than a pre-selected value:
• identify a new attribute in the new media file that is not included in the recommendation model,
• update, using a first machine learning model training step, a first coefficient in the recommendation model based on a value of the new attribute,
• score multiple media files in a user-related search history of the user with the recommendation model based on a distance for each of the multiple media files in a multi-dimensional space defined by classifiers to form a ranked list based on the updated first coefficient, and
• update, using a second machine learning model training step, a second coefficient in the recommendation model when the new media file is not at a top position of the ranked list, relative to the multiple media files in the user-related search history;
• and
• store the updated recommendation model in a training database.

A system comprising:

• a processor;
• and
• a non-transitory computer readable medium storing a set of instructions, which when executed by the processor, configure the system to:
• determine a style preference score for a new media file based on a recommendation model, wherein the new media file is selected by a user, wherein the recommendation model is a machine learning model using a neural network;
• and
• when the style preference score is lower than a pre-selected value:
• identify a new attribute in the new media file that is not included in the recommendation model,
• update, using a first machine learning model training step, a first coefficient in the recommendation model based on a value of the new attribute,
• score multiple media files in a user-related search history of the user with the recommendation model based on a distance for each of the multiple media files in a multi-dimensional space defined by classifiers to form a ranked list based on the updated first coefficient, and
• update, using a second machine learning model training step, a second coefficient in the recommendation model when the new media file is not at a top position of the ranked list, relative to the multiple media files in the user-related search history;
• and
• store the updated recommendation model in a training database.