Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation |
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| Affiliation: | 1. Arizona State University, Tempe, AZ, USA;2. University of Texas-Pan American, USA;3. University of Illinois, Chicago, USA;1. Barcelona Supercomputing Center, Barcelona Tech/Universitat Politècnica de Catalunya, Spain;1. School of Computer Science and Technology, Taiyuan University of Science and Technology, China;2. State Key Laboratory of Intelligent Control and Management of Complex System, Institute of Automation, Chinese Academy of Sciences, China;3. Swinburne University of Technology, Melbourne, Australia;1. Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan;2. Electronic Commerce Research Center, and Information Technologies Research Center, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan |
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| Abstract: | 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. |
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