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: 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: A fulfillment intent system can include a computing device configured to receive an indication of an event occurring from a user device and obtain a set of historical data associated with a user identifier indicated by the user device. The computing device is further configured to determine a fulfillment parameter by applying a machine learning model to the set of historical data and obtain a set of item identifiers based on the indication. The computing device is also configured to organize the set of item identifiers based on the fulfillment parameter and transmit the set of item identifiers to the user device for display on a user interface of the user device.

Abstract: A consideration intent system can include a computing device configured to receive an indication of an event occurring from a user device, obtain a set of parameters associated with the event and retrieve a set of item intent values corresponding to the set of items. The computing device is configured to determine a first value based on at least one parameter of the set of parameters and classify the event as one of: (i) low consideration intent and (ii) high consideration intent by inputting the set of item intent values and the first value as features to a machine learning algorithm. The computing device is configured to, based on the classification, identify a set of recommendation models, generate a set of recommended item identifiers by implementing at least one recommendation model of the set of recommendation models, and transmit the set of recommended item identifiers to the user device.

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.