1. A computer-implemented method comprising: receiving, by a term pre-processor module of a server-based automated speech recognition system that includes (a) the term pre-processor module, (b) a user similarity determiner module, (c) a vocabulary generator module, (d) a language model biaser module, and (e) an automated speech recognizer, and from a device associated with a target user, (i) data including a set of terms associated with the target user that includes terms from one or more queries previously submitted by the target user, and, (ii) from each of multiple other users, data including a set of terms associated with the other user; selecting, by the user similarity determiner module of the server-based automated speech recognition system, a particular other user based at least on comparing the set of terms associated with the target user to the sets of terms associated with the other users; selecting, by the vocabulary generator module of the server-based automated speech recognition system, one or more terms from the set of terms that is associated with the particular other user; obtaining, by the language model biaser module of the server-based automated speech recognition system and based on the selected terms that are associated with the particular other user, a biased language model; using, by the automated speech recognizer of the server-based automated speech recognition system, the biased language model generate a speech recognition output; and providing, by the server-based automated speech recognition system, the speech recognition output to the device associated with the target user.
1. A computer-implemented method comprising: receiving, by a term pre-processor module of a server-based automated speech recognition system that includes (a) the term pre-processor module, (b) a user similarity determiner module, (c) a vocabulary generator module, (d) a language model biaser module, and (e) an automated speech recognizer, and from a device associated with a target user, (i) data including a set of terms associated with the target user that includes terms from one or more queries previously submitted by the target user, and, (ii) from each of multiple other users, data including a set of terms associated with the other user; selecting, by the user similarity determiner module of the server-based automated speech recognition system, a particular other user based at least on comparing the set of terms associated with the target user to the sets of terms associated with the other users; selecting, by the vocabulary generator module of the server-based automated speech recognition system, one or more terms from the set of terms that is associated with the particular other user; obtaining, by the language model biaser module of the server-based automated speech recognition system and based on the selected terms that are associated with the particular other user, a biased language model; using, by the automated speech recognizer of the server-based automated speech recognition system, the biased language model generate a speech recognition output; and providing, by the server-based automated speech recognition system, the speech recognition output to the device associated with the target user. 2. The method of claim 1 , wherein selecting a particular other user based at least on comparing the set of terms associated with the target user to the sets of terms associated with the other users comprises selecting multiple particular other users based at least on comparing the set of terms associated with the target user to the sets of terms associated with the multiple other particular users.