Modern search engines generally provide the method of expressing queries with a few keywords for their users, which makes convenience for the common users but also difficulty to retrieve the needed information for the search engines because it is difficult for them to capture the information needs of the users exactly based on a few keywords. Therefore, query recommendation as such a technology to alleviate the difficulty begins its applications in the popular search engines of nowadays. However, almost all the approaches of query recommendation proposed until today are based on wisdom of crowds, using search logs as information source and mining behavior patterns of the users related to query construction and semantic correlation between queries. Such approach does not consider the personalized preference of information with respect to different users, and furthermore, the recommendation computing performed on the server side would impact the response efficiency and the throughput of a search engine. In this paper a strategy of personalized query recommendation running on the client side is proposed. The strategy makes use of a users browsing history as the information source and learns information preference of a user from the information source based on LDA（Latent Dirichlet Allocation）modeling. When an original query is submitted by a user, the search intention of the user is captured by the probability distribution of generating the original query from the learned LDA model, and the correlation between a term and the captured search intention is evaluated as the recommendation strength of the term with respect to the original query. The strongest Top N terms are selected as the final recommended expanded queries for the original query. The experimental validation of the proposed algorithm was performed on a test data set annotated manually. The experimental results show that the algorithm is superior to the approaches based solely on the semantic correlation between terms and original queries with respect to the accuracy of recommending expanded queries.
