Abstract: A personalized recommendation system can include a computing device configured to receive an indication of a selection from a user device identifying an item, a user, and a third-party. The computing device is configured to obtain historical data for the user and third-party data for the third-party, generate a user representation based on the historical data, and identify a set of items associated with the third-party based on the item. The computing device is configured to obtain attributes for each item and, for each item of the set of items, determine a corresponding ranking based on the third-party data, the user representation, and attributes for the corresponding item. The computing device is configured to organize a display of the set of items based on the corresponding ranking of each item and transmit the organized display of the set of items to the user device for display on a user interface.

Abstract: An approach is disclosed for providing item-to-item recommendations. The approach receives data for an item. The approach generates recommended candidate item data for the data of the received item. The approach generates feature data of the recommended candidate item data and the data of the received item. The feature data may include one or more similarities between the data of the received item and data corresponding to a respective recommended candidate item. The approach generates ranked recommended candidate item data based on the generated feature data and at least one weight determined for candidate test item data. The candidate test item data may include data corresponding to the recommended candidate item data.

Abstract: This application relates to apparatus and methods for automatically determining and providing recommendations of items to advertise customers. In some examples, a computing device generates feature data based on historical website interaction data, historical transaction data, and item categorical data. The computing device trains each of a plurality of machine learning models based on the generated feature data. The computing device may then receive a plurality of recommended items to advertise in association with an anchor item. The computing device may execute the trained machine learning process to generate prediction data associated with a future time period. The prediction data may identify a number of times each recommended item may be purchased during the future time period. The computing device may then rank the plurality of recommended items based on the prediction data. In some examples, the computing device filters the plurality of recommended items based on item categories.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions comprising: receiving one or more vectors representing one or more types of features for a pair of items; generating, using a similarity item model of a machine learning architecture, a prediction for a similar item, wherein the similarity item model combines a pair of separately trained machine learning models; combining a first output of the gradient boosted model and a second output of the neural network model to generate a similarity score for the pair of items; and transmitting the similar item to a first position on a carousel display of a website that concurrently displays the anchor item on the website. Other embodiments are disclosed.

Abstract: An approach is disclosed for providing item-to-item recommendations. The approach receives data for an item. The approach generates recommended candidate item data for the data of the received item. The approach generates feature data of the recommended candidate item data and the data of the received item. The feature data may include one or more similarities between the data of the received item and data corresponding to a respective recommended candidate item. The approach generates ranked recommended candidate item data based on the generated feature data and at least one weight determined for candidate test item data. The candidate test item data may include data corresponding to the recommended candidate item data.

Abstract: An approach is disclosed for providing enhanced complementary recommendations. The approach receives an anchor item. The approach determines one or more items similar to the anchor item, based on at least one of co-view data and content data of the anchor item. The approach determines one or more items that complement the one or more similar items, based on co-purchase data of the one or more similar items. The approach generates recommended complementary item data for the anchor item. The approach generates the recommended complementary item data based on the co-purchase data and at least one of the co-view data and the content data.

Abstract: This application relates to apparatus and methods for automatically determining and providing recommendations of items to advertise customers. In some examples, a computing device generates feature data based on historical website interaction data, historical transaction data, and item categorical data. The computing device trains each of a plurality of machine learning models based on the generated feature data. The computing device may then receive a plurality of recommended items to advertise in association with an anchor item. The computing device may execute the trained machine learning process to generate prediction data associated with a future time period. The prediction data may identify a number of times each recommended item may be purchased during the future time period. The computing device may then rank the plurality of recommended items based on the prediction data. In some examples, the computing device filters the plurality of recommended items based on item categories.