The engine works by recognising patterns within the data we gather from the Market, and translates these dynamically into suggestions for every item, which are updated daily as new information becomes available. For example, if a particular theme is commonly purchased by users who also buy one of a number of photos or audio tracks which complement the theme, we’re more likely to suggest those relevant items to buyers who have purchased the theme. This isn’t the only data we take into consideration however, we also consider things like purchase history, ratings and bookmarks, and furthermore, we’re constantly adjusting the weights given to each in order to ensure the most relevant results.