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Query recommendation has been recognized as an important mean to help users search and also improve the usability of search engines. Existing approaches mainly focus on helping users refine their search queries and the recommendations typically stick to users' search intent, named search interests in this paper. However, users may also have some vague or delitescent interests which they are unaware of until they are faced with one, named exploratory interests. These interests may be provoked within a search session when users read a web page from search results or even follow links on the page. By considering exploratory interests in query recommendation, we attract more user clicks on recommendations. This type of query recommendation has not been explicitly addressed in previous work. In this paper, we propose to recommend queries in a structured way for better satisfying both search and exploratory interests of users. Specifically, we construct a query relation graph from query logs and social annotation data which capture two types of interests respectively. Based on the query relation graph, we employ hitting time to rank possible recommendations, leverage a modularity based approach to group top recommendations into clusters, and label each cluster with social tags. Empirical experimental results indicate that our structured approach to query recommendation with social annotation data can better satisfy users' interests and significantly enhance users' click behavior on recommendations.