Abstract: A method including determining, in real-time, a diversity preference score for a user based at least in part on an anchor item chosen by the user via a user interface executed on a user device of the user. The method also can include determining, in real-time, a comparison result between the diversity preference score and a diversity preference threshold. The method further can include generating, in real-time, a personalized recommendation pool based on (a) the comparison result, (b) a complementary recommendation pool generated based at least in part on the anchor item, and (c) a diversity objective function. In many embodiments, when the comparison result indicates that the diversity preference score is greater than the diversity preference threshold, the diversity objective function can be associated with cross-domain diversity.

Abstract: Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.

Abstract: Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.

Abstract: A computer-implemented method including determining an anchor product type for an anchor item. The method further can include determining at least one associated product type for the anchor product type.

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 operations: generating, using a trained machine-learning model, personalized product-type metrics for a user based on historic activity of the user and product-type pairs in an item taxonomy; determining top product types for the user based on an anchor item; determining a set of first items associated with the top product types; ranking each item in the set of first items for (i) the anchor item and (ii) for each item in the set of first items; and selecting, based on the ranking, a set of top items from the set of first items to be personalized complementary item recommendations for the user based on the anchor item. Other embodiments are described.

Abstract: Systems and methods for post transaction seasonal item recommendations are disclosed. In some embodiments, a current seasonal time window associated with a seasonal event and some seasonal product types is determined. Based on historical transaction data of the seasonal product types, a first seasonal index score is computed for each item, and a second seasonal index score is computed for each product type including one or more items. A seasonal rank score is generated for each item based on the first seasonal index score and the second seasonal index score, such that the items in the historical transaction data are ranked based on their respective seasonal rank scores. Based on the ranked items and a transaction order from a user, a list of recommended items is generated and displayed to the user.

Abstract: Systems and methods for providing noise-resistant complementary item recommendations are disclosed. A trained model is generated based on transaction data to represent each item of a set of items as a Gaussian distribution with a mean vector and a non-zero covariance matrix. An anchor item is to be displayed to a user via a user interface executed on a user device of the user, and is represented as a Gaussian distribution with an anchor mean vector and an anchor non-zero covariance matrix. A complementarity score for each item is computed based on a distance between the mean vector of the item and the anchor mean vector to generate a ranking for the set of items based on their respective complementarity scores. A plurality of top items are selected from the set of items based on the ranking as recommended complementary items, which are displayed with the anchor item on the user interface.

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 generating personalized product-type metrics for the user based at least in part on a user embedding for the user and product-type embedding Gaussian mixture distributions; determining top product types based at least in part on personalized product-type complementarity metrics generated using the personalized product-type metrics and cosine similarity measurements; generating a set of first items associated with the top product-types; ranking each respective item in the set of first items generated using an item-level embedding Gaussian distribution for the anchor item and a respective item-level embedding Gaussian distribution for the each respective item; and selecting a set of top items as the personalized complementary item recommendations based on the ranking. Other embodiments are disclosed.

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: mapping each item of multiple items in a mixed-intent basket to a respective product type code (PT code); generating a respective list of complementary product type codes from each respective PT code; generating, using a complementary item algorithm, a respective candidate set of complementary items; detecting a platform-level configuration of a platform used by an electronic device of a user; loading, using diversity rotation, the respective quantity of complementary items onto a website carousel; and displaying the website carousel, as loaded, on the electronic device of the user, wherein the website carousel is sized to fit the platform-level configuration. Other embodiments are disclosed.

Abstract: Systems and methods for generating and using seasonal affinity scores is disclosed. A set of user-specific historical transaction data is obtained and a user-specific affinity score including at least one of a user-specific season affinity score or a user-specific seasonal theme affinity score is determined by determining one or more product affinity scores for a set of product taxonomies and combining the one or more product affinity scores with one or more product index scores to generate the user-specific affinity score. The product affinity scores are determined by a trained scoring calculation model configured to receive the set of user-specific historical transaction data. One or more interface elements are selected based on the user-specific affinity score and an interface is generated including the one or more interface elements.

Abstract: A seasonal recommendation system can include a computing device that is configured to receive a request to identify a set of recommendations associated with a season, obtain historical data over a threshold period for a set of product types, and compute a seasonality index score based on the historical data over a target period and the threshold period. The computing device is also configured to select a subset of product types based on the seasonality index score and by applying a theme-aware model to the product types and identify and store a set of items corresponding to at least one product type of the subset of product types. The computing device is configured to, in response to a user navigating to a webpage using a user device, select and display at least one item of the set of items on a user interface of the user device.

Abstract: Systems and methods for generating a set of personalized complementary recommendations is disclosed. A user identifier and an anchor item identifier are received. A set of personalized-weighted items and a set of complimentary-weighted items are each generated based on the user identifier and the anchor item identifier. The personalized-weighted items are generated by a trained supervised model. The complementary-weighted items are generated by a trained unsupervised model. A set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items is generated.

Abstract: This application relates to apparatus and methods for automatically determining and providing item advertisements to customers. In some examples, a computing device obtains transaction data identifying in-store and/or online transactions. The computing device determines a distribution of purchased items over a plurality of item categories based on the transaction data. The computing device generates factorization matrices based on applying a machine learning process to the distribution, and generates relevancy scores for the plurality of item categories based on the factorization. The computing device may then select or generate item advertisements for items associated with the item categories based on the generated relevancy scores. The selected item advertisements may be displayed to a customer, for example, on a website.

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 generating personalized product-type metrics for the user based at least in part on a user embedding for the user and product-type embedding Gaussian mixture distributions; determining top product types based at least in part on personalized product-type complementarity metrics generated using the personalized product-type metrics and cosine similarity measurements; generating a set of first items associated with the top product-types; ranking each respective item in the set of first items generated using an item-level embedding Gaussian distribution for the anchor item and a respective item-level embedding Gaussian distribution for the each respective item; and selecting a set of top items as the personalized complementary item recommendations based on the ranking. Other embodiments are disclosed.

Abstract: A method including determining, in real-time, a diversity preference score for a user based at least in part on an anchor item chosen by the user via a user interface executed on a user device of the user. The method also can include determining, in real-time, a comparison result between the diversity preference score and a diversity preference threshold. The method further can include generating, in real-time, a personalized recommendation pool based on (a) the comparison result, (b) a complementary recommendation pool generated based at least in part on the anchor item, and (c) a diversity objective function. In many embodiments, when the comparison result indicates that the diversity preference score is greater than the diversity preference threshold, the diversity objective function can be associated with cross-domain diversity.

Abstract: This application relates to apparatus and methods for automatically determining and providing item category advertisement recommendations. In some examples, a computing device obtains transaction data identifying historical transactions. The computing device generates a first model, and trains the first model with non-seasonal data. The computing device generates a second model, and trains the second model with seasonal data. The computing device then generates a seasonal re-rank model based on the first model and the second model. The seasonal re-rank model, when executed, identifies probabilities of purchase of categories of items. In some examples, the computing device selects item advertisements to provide for display to a customer based on the probabilities of purchase of categories of items determined by the seasonal re-rank model. The selected item advertisements may be displayed to the customer, for example, on a website.

Abstract: Systems and methods for generating a set of personalized complementary recommendations is disclosed. A user identifier and an anchor item identifier are received. A set of personalized-weighted items and a set of complimentary-weighted items are each generated based on the user identifier and the anchor item identifier. The personalized-weighted items are generated by a trained supervised model. The complementary-weighted items are generated by a trained unsupervised model. A set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items is generated.

Abstract: This application relates to apparatus and methods for automatically determining and providing item category advertisement recommendations. In some examples, a computing device obtains transaction data identifying historical transactions. The computing device generates a first model, and trains the first model with non-seasonal data. The computing device generates a second model, and trains the second model with seasonal data. The computing device then generates a seasonal re-rank model based on the first model and the second model. The seasonal re-rank model, when executed, identifies probabilities of purchase of categories of items. In some examples, the computing device selects item advertisements to provide for display to a customer based on the probabilities of purchase of categories of items determined by the seasonal re-rank model. The selected item advertisements may be displayed to the customer, for example, on a website.

Abstract: This application relates to apparatus and methods for automatically determining and providing item advertisements to customers. In some examples, a computing device obtains transaction data identifying in-store and/or online transactions. The computing device determines a distribution of purchased items over a plurality of item categories based on the transaction data. The computing device generates factorization matrices based on applying a machine learning process to the distribution, and generates relevancy scores for the plurality of item categories based on the factorization. The computing device may then select or generate item advertisements for items associated with the item categories based on the generated relevancy scores. The selected item advertisements may be displayed to a customer, for example, on a website.