RSDC’09: Tag Recommendation Using Keywords and Association Rules

While a webpage usually contains hundreds of words, there are only two to three tags that would typically be assigned to this page. Most tags could be found in related aspects of the page, such as the page own content, the anchor texts around the page, and the user’s own opinion about the page. Thus it is not an easy job to extract the most appropriate two to three tags to recommend for a target user. In addition, the recommendations should be unique for every user, since everyone’s perspective for the page is different. In this paper, we treat the task of recommending tags as to find the most likely tags that would be chosen by the user. We first applied the TF-IDF algorithm on the limited description of the page content, in order to extract the keywords for the page. Based on these top keywords, association rules from history records are utilized to find the most probable tags to recommend. In addition, if the page has been tagged before by other users or the user has tagged other resources before, that history information is also exploited to find the most appropriate recommendations.

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