Abstract: A machine learning engine may be used to identify items in a second item category that have a visual appearance similar to the visual appearance of a first item selected from a first item category. Image data and text data associated with a large number of items from different item categories may be processed and used by an association model created by a machine learning engine. The association model may extract item attributes from the image data and text data of the first item. The machine learning engine may determine weights for parameter types, and the weights may calibrate the influence of the respective parameter types on the search results. The association model may be deployed to identify items from different item categories that have a visual appearance similar to the first item. The association model may be updated over time by the machine learning engine as data correlations evolve.