System, method, and non-transitory computer readable medium for generating recommendations

A computer-implemented method, comprising:

• receiving an item recommendation request corresponding to an asset from a computing device;
• selecting, based on the item recommendation request, a set of item identifiers of a plurality of item identifiers, an associated published timeframe of the selected item identifiers being related to a present timeframe, the plurality of item identifiers and a plurality of asset identifiers being stored in a database, each item identifier of the plurality of item identifiers being associated with item data and a published timeframe, and each asset identifier of the plurality of asset identifiers being associated with asset data and a previously published indicator;
• receiving historical interaction data associated with the asset, wherein the historical interaction data is generated by a click tracking module that records user interactions with at least the asset, and wherein the click tracking module associates the asset with user interactions if a threshold number of user interactions is not exceeded;
• generating a respective image score representative of a similarity between an item image associated with the item data and an asset image associated with the asset based on a first trained machine learning model, wherein the first trained machine learning model is based on unsupervised linear discriminant analysis to reduce one or more dimensions across extracted feature vectors associated with the item image and the asset image;
• determining a composite similarity value for each item identifier of the set of item identifiers comparing a similarity of the asset to each item identifier of the set of item identifiers, wherein the composite similarity value comprises a harmonic mean of:
• an order score representative of a likelihood of interaction with the item data of a corresponding item identifier, wherein the order score comprises a first historical order score when the historical interaction data includes user interactions indicating a relevance between an item identifier of the set of item identifiers and the asset, and wherein the order score comprises a predicted order score when the historical interaction data does not include user interactions indicating a relevance between an item identifier of the set of item identifiers and the asset;
• a string score representative of a similarity of the item data of the corresponding item identifier and the asset data of the asset;
• a keyword score representative of a similarity between keywords associated with the item data and an asset name associated with the asset;
• and
• the respective image score representative of the similarity between the item image associated with the item data and the asset image associated with the asset,
• wherein the string score or the keyword score is generated by a second trained machine learning model utilizing a first labeled training dataset comprising historically identified relevant items for the asset and validation labels;
• generating an item recommendation list including each item identifier of the set of item identifiers with the respective composite similarity value above a threshold value;
• transmitting instructions to the computing device that cause the computing device to display a user interface including interface elements representative of the item recommendation list;
• receiving feedback data representative of additional user interactions with the displayed user interface including validation or rejection of at least one item identifier in the item recommendation list;
• generating an updated second trained machine learning model based on a second labeled training dataset, wherein the second labeled training dataset is generated by removing items from the first labeled training dataset based on the feedback data, and the first trained machine learning model and the second trained machine learning model include a plurality of processing layers, each processing layer of the plurality of processing layers associated with trainable filters, transformations, projections, hashing, pooling, and regularization;
• and
• generating an updated item recommendation list for display including an updated set of item identifiers based on updated composite similarity values, wherein each of the updated composite similarity values includes at least one updated score generated by the updated second trained machine learning model and an updated order score comprising a second historical order score generated at least in part based on the feedback data, wherein the updated set of item identifiers is different than the set of item identifiers.

A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by at least one processor, cause a device to perform operations comprising:

• receiving an item recommendation request corresponding to an asset from a computing device;
• selecting, based on the item recommendation request, a set of item identifiers of a plurality of item identifiers, an associated published timeframe of the selected item identifiers being related to a present timeframe, the plurality of item identifiers and a plurality of asset identifiers being stored in a database, each item identifier of the plurality of item identifiers being associated with item data and a published timeframe, and each asset identifier of the plurality of asset identifiers being associated with asset data and a previously published indicator;
• receiving historical interaction data associated with the asset, wherein the historical interaction data is generated by a click tracking module that records user interactions with at least the asset, and wherein the click tracking module associates the asset with user interactions if a threshold number of user interactions is not exceeded;
• generate a respective image score representative of a similarity between an item image associated with the item data and an asset image associated with the asset based on a first trained machine learning model, wherein the first trained machine learning model is based on unsupervised linear discriminant analysis to reduce one or more dimensions across extracted feature vectors associated with the item image and the asset image;
• determining a composite similarity value for each item identifier of the set of item identifiers comparing a similarity of the asset to each item identifier of the set of item identifiers, wherein the composite similarity value comprises a harmonic mean of:
• an order score representative of a likelihood of interaction with the item data of a corresponding item identifier, wherein the order score comprises a first historical order score when the historical interaction data includes user interactions indicating a relevance between an item identifier of the set of item identifiers and the asset, and wherein the order score comprises a predicted order score when the historical interaction data does not include user interactions indicating a relevance between an item identifier of the set of item identifiers and the asset;
• a string score representative of a similarity of the item data of the corresponding item identifier and the asset data of the asset;
• a keyword score representative of a similarity between keywords associated with the item data and an asset name associated with the asset;
• and
• the respective image score representative of the similarity between the item image associated with the item data and the asset image associated with the asset,
• wherein the string score or the keyword score is generated by a second trained machine learning model utilizing a first labeled training dataset comprising historically identified relevant items for the asset and validation labels;
• generating an item recommendation list including each item identifier of the set of item identifiers with the respective composite similarity value above a threshold value;
• transmitting instructions to the computing device that cause the computing device to display a user interface including interface elements representative of the item recommendation list;
• receiving feedback data representative of additional user interactions with the displayed user interface including validation or rejection of at least one item identifier in the item recommendation list;
• generating an updated second trained machine learning model based on a second labeled training dataset, wherein the second labeled training dataset is generated by removing items from the first labeled training dataset based on the feedback data, and the first trained machine learning model and the second trained machine learning model include a plurality of processing layers, each processing layer of the plurality of processing layers associated with trainable filters, transformations, projections, hashing, pooling, and regularization;
• and
• generating an updated item recommendation list for display including an updated set of item identifiers based on respectively updated composite similarity values, wherein each of the updated composite similarity values includes at least one updated score generated by the updated second trained machine learning model and an updated order score comprising a second historical order score generated at least in part based on the feedback data, wherein the updated set of item identifiers is different than the set of item identifiers.