1. A method comprising: accessing a community preference dataset representing preferences of a community of users regarding a plurality of objects, wherein the community preference dataset includes a plurality of user-specified paired comparisons regarding the plurality of objects; identifying a first user category in the community of users; generating a ranking model and a baseline mixing weight for the first user category based on the community preference dataset; accessing an individual preference dataset representing preferences of a first user of the first user category regarding at least one of the plurality of objects; generating a personalized mixing weight for the first user based on the individual preference dataset and the ranking model for the first user category; adjusting the personalized mixing weight for the first user based on a weighted average of the baseline mixing weight for the first user category and the personalized mixing weight for the first user, wherein the weighting of the weighted average is based on a size of the individual preference dataset; and ranking at least two of the plurality of objects based on the adjusted personalized mixing weight for the first user and the ranking model for the first user category.
3. The method of claim 1 , wherein the generating of the ranking model for the first user category comprises maximizing a log-likelihood: 
 log Π i=1 N p ( x i ,u i ), wherein x i is at least one data element from the community preference dataset, u i is at least one user of the first user category, p(x i , u i ) is a probability that x i agrees with u i , and N is a number of data elements in the community preference dataset.