1. A method of creating a personalized recommendation of an item, the method comprising: using one or more processors: creating a first plurality of probability distributions based upon a dataset that includes a plurality of users and a list of search queries that each user in the plurality of users submitted over a predetermined period of time, each of the first plurality of probability distributions corresponding to a respective one of a plurality of latent topics and describing, for each respective search query in the list of search queries, a probability that the respective latent topic is related to the respective search query; creating a user-based probability distribution for a user based upon the first plurality of probability distributions and a second list of search queries previously entered by the user, the user-based probability distribution corresponding to the user and describing a probability that the user is interested in each of the plurality of latent topics; determining a high-ranking latent topic from the user-based probability distribution; determining a high-ranking search query from one of the first plurality of probability distributions corresponding to the determined high-ranking latent topic; submitting the high-ranking search query to a search engine of an electronic marketplace; and recommending to the user, at least one search result received from the search engine of the electronic marketplace in response to the submitted high-ranking search query, the at least one search result describing a volatile item that is part of an item inventory on a temporary basis.