Abstract
With the development of social websites during the web2.0, tagging has been playing an important role for users to mark their web resource. By offering personalized tags, recommender systems help users to integrate resource and complete tagging effectively. Graph based methods of tags recommending have been shown to provide high quality results such as RWR, FolkRank, PageRank, etc. However, data sparsity leads to sparseness of graphs limiting the precision in the process of use. In this paper, we propose a new method ARRW to alleviate the sparsity problem. We introduce Association Rules to Random Walk for digging up more relevance among nodes in graphs. We evaluate ARRW on a real-world dataset collected on Delicious. Data tests show that ARRW outperforms other Random Walk methods which not consider intra-relations, and ARRW successfully alleviate the graph sparsity problem.
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Wang, J., Luo, N. (2015). Association-Rule-Based Random Walk Method for Personalized Tag Recommendation. In: Abraham, A., Jiang, X., Snášel, V., Pan, JS. (eds) Intelligent Data Analysis and Applications. Advances in Intelligent Systems and Computing, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-21206-7_1
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DOI: https://doi.org/10.1007/978-3-319-21206-7_1
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