21. A non-transitory machine readable storage medium having computer readable program code embedded therein for optimizing product recommendations, comprising: a content page module to provide a product page for display, including recommendations from a similarities dataset selected from a plurality of similarities datasets, wherein the similarities dataset 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 similarities dataset 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 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 context item processor to receive and track context items associated with a display of the product page; a join processor to join the context items associated with the display of the product page; a recommendations modeler to create a recommendations model based on the context items joined by the join processor and the similarities dataset selected; and a model data store to store the recommendations model.
21. A non-transitory machine readable storage medium having computer readable program code embedded therein for optimizing product recommendations, comprising: a content page module to provide a product page for display, including recommendations from a similarities dataset selected from a plurality of similarities datasets, wherein the similarities dataset 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 similarities dataset 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 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 context item processor to receive and track context items associated with a display of the product page; a join processor to join the context items associated with the display of the product page; a recommendations modeler to create a recommendations model based on the context items joined by the join processor and the similarities dataset selected; and a model data store to store the recommendations model. 22. The system of claim 21 , further comprising a recommendations logger to log the recommendations and the similarities dataset selected.