14. A non-transitory machine readable storage medium having computer readable program code embedded therein for providing product recommendations, comprising: a context item processor to receive and track context items as a product page is navigated; a plurality of similarities datasets comprising products with a similarity to a product on the product page, each of the plurality of similarities datasets being derived from a separate source; a data store selection module to select one of the plurality of similarities datasets as a source of product recommendations based on the context items, wherein the one of the plurality of similarities datasets is selected using a machine learning model that yields patterns representative of an underlying mechanism based on the context items as input for the machine model, and wherein the plurality of similarities datasets are prepared for the product page, and the machine learning model selects the one of the plurality of similarities datasets based on a fallback strategy where one of the plurality of similarity datasets is a new similarities dataset, wherein the fallback strategy is learned by the machine learning model using logged data regarding the plurality of similarities datasets used for past recommendations; a logging module to log the similarities dataset selected by the machine learning model for use in making future recommendations; a ranking module to rank products in the selected one of the plurality of similarities datasets based on the context items; and a content page module to provide the product recommendations for display based on the context items.
19. The system of claim 14 , wherein the context items comprise an access event accessing the product page using an identified account associated with preferences or customer segment data.