1. A method comprising: retrieving, over a network at a computing device, descriptive data relating to at least a subset of a plurality of entities on a web site, the descriptive data comprising a plurality of descriptive data entries, each descriptive data entry relating to a respective one of the subset of the plurality of entities; retrieving, over the network at the computing device, from the website, endorsement data relating to the plurality of entities, the endorsement data comprising a plurality of endorsements, each endorsement relating to a respective one of the plurality of entities and a respective one of a plurality of users; determining, via the computing device, based on the descriptive data and the endorsement data, a first set of probabilities, each of the first set of probabilities reflecting a probability that a respective one of the plurality of endorsements is attributable to a respective one of a plurality of aspects determined from a sampling of a respective aspect value at an assigned term-slot, each of the plurality of aspects representing a cluster of entities; determining, via the computing device, based on the descriptive data and the endorsement data, a second set of probabilities, each of the second set of probabilities reflecting a probability that a respective one of a plurality of terms is attributable to a respective one of the plurality of aspects; selecting, via the computing device, based on the first set of probabilities and the second set of probabilities, for each entity, a respective subset of the plurality of terms that are most probably associated with the respective entity; and generating, via the computing device, for each entity, based on the respective subset of the plurality of terms that are most probably associated with the respective entity, a respective set of tags, each tag reflecting one of the subset of the plurality of terms.
1. A method comprising: retrieving, over a network at a computing device, descriptive data relating to at least a subset of a plurality of entities on a web site, the descriptive data comprising a plurality of descriptive data entries, each descriptive data entry relating to a respective one of the subset of the plurality of entities; retrieving, over the network at the computing device, from the website, endorsement data relating to the plurality of entities, the endorsement data comprising a plurality of endorsements, each endorsement relating to a respective one of the plurality of entities and a respective one of a plurality of users; determining, via the computing device, based on the descriptive data and the endorsement data, a first set of probabilities, each of the first set of probabilities reflecting a probability that a respective one of the plurality of endorsements is attributable to a respective one of a plurality of aspects determined from a sampling of a respective aspect value at an assigned term-slot, each of the plurality of aspects representing a cluster of entities; determining, via the computing device, based on the descriptive data and the endorsement data, a second set of probabilities, each of the second set of probabilities reflecting a probability that a respective one of a plurality of terms is attributable to a respective one of the plurality of aspects; selecting, via the computing device, based on the first set of probabilities and the second set of probabilities, for each entity, a respective subset of the plurality of terms that are most probably associated with the respective entity; and generating, via the computing device, for each entity, based on the respective subset of the plurality of terms that are most probably associated with the respective entity, a respective set of tags, each tag reflecting one of the subset of the plurality of terms. 11. The method of claim 1 where each of the second set of probabilities is p(t|k), determined using a term-aspect distribution as follows: 
 Φ:{φ kt =p ( t|k )∀ k εK,tευ} where Φ is a term-aspect distribution for all terms and aspects, φ kt is a term-aspect distribution for a particular term t and aspect k, K is the plurality of aspects; υ is a vocabulary of terms.