Abstract: A systems 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, perform: receiving in-session user activity information, the in-session user activity information comprising a user search query from a user and a ranked list of products to be displayed to the user based on the user search query, and a product of the products being part of a boost category; determining a respective content model prediction score for each of the products of the ranked list of the products; determining a respective discounted cumulative gain (DCG) score for each of the products of the ranked list of the products; analyzing the product that is part of the boost category to determine if the product is to be repositioned within the ranked list of the products based on the respective DCG score for each of the products; and in response to determining that the product that is part of the boost category is to be repositione

Abstract: This application relates to apparatus and methods for training machine learning models, and applying the trained models to determine an item's relevance to a search query. In some examples, features are generated based on item engagement data associated with a search query. First values are generated for each of a plurality of items based on the search query, a first attribute of each of the items, and the item engagement data. Second values are generated for the items based on the search query, the first attributes, a second attribute of each item, and the item engagement data. Third values are generated for the items based on the search query, the first attributes, a third attribute of each item, and the item engagement data. Features are generated based on the first, second, and third values, and a machine learning model is applied to the features to rank items.