Abstract: This application relates to apparatus and methods for automatically determining item relevancy based on textual information. In some examples, a computing device receives a search query, and a plurality of items corresponding to the search query. The computing device may identify one or more features of the search query. The computing device may generate relevancy values for each of the items based on the features of the search query, and features of each of the plurality of items. For example, the computing device may generate, for each of the items, a plurality of relevance values, each relevance value generated based on a feature of the search query and corresponding features of the item. The computing device may transmit the generated relevancy values for the plurality of items. In some examples, the computing device may rank the plurality of items based on the generated relevancy values.

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 including: receiving a respective item description and at least one respective attribute value for each item of a set of items; generating at least one respective text embedding; generating a graph of the set of items based on at least co-view data to create pairs of items that are co-viewed by joining respective pairs of items; training the text embedding model and a machine learning model using a neural loss function based on the graph; and automatically determining, using the machine learning model, as trained, a label for each item of the set of items. Other embodiments are disclosed.

Abstract: A seasonal recommender system includes a computing device configured to obtain periodic sales data characterizing a number of purchases made of each item of a plurality of items in a specified period and to obtain periodic buyers data characterizing a number of unique customers of each item in the plurality of items in the specified period. The computing device is further configured to determine a final item seasonality embedding for each item based on the periodic sales data and the periodic buyers data and to determine a final user seasonality embedding for each user based on the periodic purchase data. The computing device is further configured to determine a final user-item score for each item based on the final item seasonality embedding and the final user seasonality embedding and to send a recommendation to a user based on the final user-item score.

Abstract: This application relates to apparatus and methods for automatically determining item relevancy based on textual information. In some examples, a computing device receives a search query, and a plurality of items corresponding to the search query. The computing device may identify one or more features of the search query. The computing device may generate relevancy values for each of the items based on the features of the search query, and features of each of the plurality of items. For example, the computing device may generate, for each of the items, a plurality of relevance values, each relevance value generated based on a feature of the search query and corresponding features of the item. The computing device may transmit the generated relevancy values for the plurality of items. In some examples, the computing device may rank the plurality of items based on the generated relevancy values.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform receiving from an item catalog database a respective item description and respective attribute values for each item of a set of items; generating text embeddings using a text embedding model to represent the respective item description and the respective attribute values; generating a graph of the set of items from the item catalog database connected by a set of edges; training the text embedding model and a machine learning model using a neural loss function based on the graph; and automatically determining, based on the machine learning model, as trained, a gender label for each first item in which the gender classification is unlabeled and in which a respective quantity of respective attribute values for the each first item is at least a predetermined threshold. Other embodiments are disclosed.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving an input identifying an anchor item; determining, using a quadruplet network associated with a neural network architecture, one or more item categories corresponding to complementary items associated with the anchor item; generating, using a ranking network associated with the neural network architecture, scores for the complementary items included in the one or more item categories; generating, using the ranking network associated with the neural network architecture, first ranking results for the complementary items based, at least in part, on the scores; and selecting one or more of the complementary items to be displayed based, at least in part, on the first ranking results. Other embodiments are disclosed herein.

Abstract: A seasonal recommender system includes a computing device configured to obtain periodic sales data characterizing a number of purchases made of each item of a plurality of items in a specified period and to obtain periodic buyers data characterizing a number of unique customers of each item in the plurality of items in the specified period. The computing device is further configured to determine a final item seasonality embedding for each item based on the periodic sales data and the periodic buyers data and to determine a final user seasonality embedding for each user based on the periodic purchase data. The computing device is further configured to determine a final user-item score for each item based on the final item seasonality embedding and the final user seasonality embedding and to send a recommendation to a user based on the final user-item score.

Abstract: This application relates to apparatus and methods for automatically determining and providing personalized digital recommendations including sponsored items. In some examples, a computing device receives a recommendation request. In response, the computing device determines an initial set of items for recommendation based on a relevance of associated items to the user and potential revenue from user interactions with the associated items. The computing device then generates final item recommendations by replacing at least one item of the initial set of items with a closest sponsored item that is selected based on a similarity of the closest sponsored item to the corresponding item. The final item recommendations are then presented to the user.

Abstract: This application relates to apparatus and methods for automatically determining item relevancy based on textual information. In some examples, a computing device receives a search query, and a plurality of items corresponding to the search query. The computing device may identify one or more features of the search query. The computing device may generate relevancy values for each of the items based on the features of the search query, and features of each of the plurality of items. For example, the computing device may generate, for each of the items, a plurality of relevance values, each relevance value generated based on a feature of the search query and corresponding features of the item. The computing device may transmit the generated relevancy values for the plurality of items. In some examples, the computing device may rank the plurality of items based on the generated relevancy values.

Abstract: This application relates to apparatus and methods for automatically determining and providing carousels specifically curated for a user. In some examples, a computing device obtains user transaction data identifying in-store and/or online transactions, and user engagement data identifying user interactions with items and carousels from user's prior sessions. The computing device determines a sequential order for presentation of carousels with a set of item recommendations. For example, the computing device scores each potential carousel based on prior user interactions and transactions with items and carousels. The carousels are then ranked and subsequently presented to the user based on their corresponding scores.

Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one or more processors and perform receiving from an item catalog database a respective item description and respective attribute values for each item of a set of items; generating text embeddings using a text embedding model to represent the respective item description and the respective attribute values; generating a graph of the set of items from the item catalog database connected by a set of edges; training the text embedding model and a machine learning model using a neural loss function based on the graph; and automatically determining, based on the machine learning model, as trained, a gender label for each first item in which the gender classification is unlabeled and in which a respective quantity of respective attribute values for the each first item is at least a predetermined threshold. Other embodiments are disclosed.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving an input identifying an anchor item; determining, using a quadruplet network associated with a neural network architecture, one or more item categories corresponding to complementary items associated with the anchor item; generating, using a ranking network associated with the neural network architecture, scores for the complementary items included in the one or more item categories; generating, using the ranking network associated with the neural network architecture, first ranking results for the complementary items based, at least in part, on the scores; and selecting one or more of the complementary items to be displayed based, at least in part, on the first ranking results. Other embodiments are disclosed herein.