34. A computer-implemented method for determining a browse relevance score for an item classified in multiple categories, the method comprising: a computing device comprising a memory, the memory storing instructions that, when executed, cause the computing device to: identify each category with which the item is classified; and for each identified category: determine a category fit score for the item; determine a browse score for the item based on the historical activities of users within a browse node that corresponds to the category; determine a prediction boost score that provides an additional score if the item is part of a market trend or the item is based on a popular theme; determine a popularity score for the item based on the popularity of the item and based at least in part upon the prediction boost score; and determine a browse relevance score for each item with respect to the category by summing at least the category fit score, the browse score, and the popularity score, and multiplying a result of the summing for each item by a price range score for the item, wherein the browse relevance score for each identified category is used to select an arrangement of the item in a display generated for any of the identified categories.
34. A computer-implemented method for determining a browse relevance score for an item classified in multiple categories, the method comprising: a computing device comprising a memory, the memory storing instructions that, when executed, cause the computing device to: identify each category with which the item is classified; and for each identified category: determine a category fit score for the item; determine a browse score for the item based on the historical activities of users within a browse node that corresponds to the category; determine a prediction boost score that provides an additional score if the item is part of a market trend or the item is based on a popular theme; determine a popularity score for the item based on the popularity of the item and based at least in part upon the prediction boost score; and determine a browse relevance score for each item with respect to the category by summing at least the category fit score, the browse score, and the popularity score, and multiplying a result of the summing for each item by a price range score for the item, wherein the browse relevance score for each identified category is used to select an arrangement of the item in a display generated for any of the identified categories. 36. The computer-implemented method of claim 34 , wherein the popularity score includes a newness score that provides an additional score if the item is new.