Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation

We propose a collaborative filtering method to provide an enhanced recommendation quality derived from user-created tags. Collaborative tagging is employed as an approach in order to grasp and filter users’ preferences for items. In addition, we explore several advantages of collaborative tagging fo...

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Veröffentlicht in:	Electronic commerce research and applications 2010, Vol.9 (1), p.73-83
Hauptverfasser:	Kim, Heung-Nam, Ji, Ae-Ttie, Ha, Inay, Jo, Geun-Sik
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Sprache:	eng
Schlagworte:	Algorithms Collaboration Collaborative filtering Collaborative tagging Electronic commerce Recommender system Studies
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container_title	Electronic commerce research and applications
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creator	Kim, Heung-Nam Ji, Ae-Ttie Ha, Inay Jo, Geun-Sik
description	We propose a collaborative filtering method to provide an enhanced recommendation quality derived from user-created tags. Collaborative tagging is employed as an approach in order to grasp and filter users’ preferences for items. In addition, we explore several advantages of collaborative tagging for data sparseness and a cold-start user. These applications are notable challenges in collaborative filtering. We present empirical experiments using a real dataset from del. icio. us. Experimental results show that the proposed algorithm offers significant advantages both in terms of improving the recommendation quality for sparse data and in dealing with cold-start users as compared to existing work.
doi_str_mv	10.1016/j.elerap.2009.08.004
format	Article
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subjects	Algorithms Collaboration Collaborative filtering Collaborative tagging Electronic commerce Recommender system Studies
title	Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation
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