Abstract
Tag recommendation systems are either personalized or non-personalized. Personalized tag recommendation utilizes a user's tagging behavior from her tagging history for predictions. Whereas non-personalized recommendation systems recommend what is popular and relevant to the user. In this study, we have analyzed the role of personal tagging history in recommending tags. The experiments are done on three folksonomy datasets: Delicious, Flickr and Bibsonomy. Important results for three popular tag recommendation algorithms: PITF, FolkRank and Adapted PageRank are reported in terms of prediction quality. It is found that users' history usage preferences change across all data sets; hence overall prediction quality of personalized recommendation system may suffer. We discover a generic life cycle of folksonomy users on the basis of their history usage. We propose this life cycle can be used to improve an overall prediction performance of a recommendation system across all folksonomies.
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