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As a social bookmark tool, Folksonomy gives high freedom to users and allows users to share and notate resources. However, many tags applied arbitrarily by users can not really reflect the contents of web pages and lead to ineffectiveness in information retrieval. Moreover, there are still some important tasks about how to eliminate ambiguity more easily and recommend more interested web pages to users. To resolve the above problems, we propose a novel mechanism named SSTAG, and it can recommend a set of Super-tags to users for their choices based on keywords input. As various topics related to the keywords, the Super-tags are selected from different clusters of web pages related to the keywords. A user chooses a Super-tag, which means the user may have chosen an interested topic, and then some more detailed tags in the topic are recommended as Sub-tags. The relationship between Super-tag and Sub-tag is just like navigation and positioning. Likewise, the user can choose one Sub-tag and submit it with the Super-tag. By means of the user's choice, this system can capture users' preference and recommend a series of related web pages. We employ a real world dataset to examine the mechanism, and the experimental results show that this mechanism can eliminate ambiguity efficiently and recommend a set of appropriate tags.