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.