Predicting Social Annotation by Spreading Activation
Abon Chen1, Hsin-Hsi Chen2, and Polly Huang1
1Department of Electrical Engineering
r94921033@ntu.edu.tw
phuang@cc.ee.ntu.edu.tw
2Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
hhchen@ntu.edu.tw
Abstract. Social bookmark services like del.icio.us enable easy annotation for users to organize their resources. Collaborative tagging provides useful index for information retrieval. However, lack of sufficient tags for the developing documents, in particular for new arrivals, hides important documents from being retrieved at the earlier stages. This paper proposes a spreading activation approach to predict social annotation based on document contents and users’ tagging records. Total 28,792 mature documents selected from del.icio.us are taken as answer keys. The experimental results show that this approach predicts 71.28% of a 100 users’ tag set with only 5 users’ tagging records, and 84.76% of a 13-month tag set with only 1-month tagging record under the precision rates of 82.43% and 89.67%, respectively.
Keywords: Collaborative Tagging, Social Annotation, Spreading Activation LNCS 4822, p. 277 ff.
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