1. A computer-implemented method for categorizing items, the method comprising: under control of one or more configured computer systems; obtaining item information regarding an item of interest; obtaining one or more hierarchically organized categories for items; determining a relevance of the item information to each of the categories; identifying one or more first category candidates and one or more second category candidates for the item of interest from the categories based at least upon the determined relevance of the item information to each of the categories; determining a first likelihood value for each pairing of an identified first category candidate and an identified second category candidate, the first likelihood value representing a statistical likelihood that the pairing of the identified first category candidate and the identified second category candidate are paired categories for the item of interest; assigning, as categories for the item of interest, the identified first category candidate and the identified second category candidate of a pairing having a first likelihood value that is greater than a threshold value; determining a second likelihood value that represents a statistical likelihood that the identified first category candidate and at least two identified second category candidates are paired categories to the item of interest; and if the second likelihood value is greater than the threshold value, assigning, as categories for the item of interest, each of the at least two identified second category candidates and the first identified category candidate.
1. A computer-implemented method for categorizing items, the method comprising: under control of one or more configured computer systems; obtaining item information regarding an item of interest; obtaining one or more hierarchically organized categories for items; determining a relevance of the item information to each of the categories; identifying one or more first category candidates and one or more second category candidates for the item of interest from the categories based at least upon the determined relevance of the item information to each of the categories; determining a first likelihood value for each pairing of an identified first category candidate and an identified second category candidate, the first likelihood value representing a statistical likelihood that the pairing of the identified first category candidate and the identified second category candidate are paired categories for the item of interest; assigning, as categories for the item of interest, the identified first category candidate and the identified second category candidate of a pairing having a first likelihood value that is greater than a threshold value; determining a second likelihood value that represents a statistical likelihood that the identified first category candidate and at least two identified second category candidates are paired categories to the item of interest; and if the second likelihood value is greater than the threshold value, assigning, as categories for the item of interest, each of the at least two identified second category candidates and the first identified category candidate. 6. The computer-implemented method of claim 1 , wherein determining a first likelihood value comprises obtaining one or more likelihood values for the pairing of an identified first category candidate and an identified second category candidate from a data store.