Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems
Contribuinte(s) |
Cao, T. Lim, E.-P. Zhou, Z.-H. Ho, T.-B. Cheung, D. Motoda, H. |
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Data(s) |
27/02/2015
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Resumo |
Discounted Cumulative Gain (DCG) is a well-known ranking evaluation measure for models built with multiple relevance graded data. By handling tagging data used in recommendation systems as an ordinal relevance set of {negative,null,positive}, we propose to build a DCG based recommendation model. We present an efficient and novel learning-to-rank method by optimizing DCG for a recommendation model using the tagging data interpretation scheme. Evaluating the proposed method on real-world datasets, we demonstrate that the method is scalable and outperforms the benchmarking methods by generating a quality top-N item recommendation list. |
Formato |
application/pdf |
Identificador | |
Publicador |
Springer-Verlag Berlin Heidelberg |
Relação |
http://eprints.qut.edu.au/83203/3/83203.pdf http://www.springer.com/gp/book/9783319180311 DOI:10.1007/978-3-319-18032-8_40 Ifada, Noor & Nayak, Richi (2015) Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems. In Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., & Motoda, H. (Eds.) Advances in Knowledge Discovery and Data Mining. Springer-Verlag Berlin Heidelberg, Berlin, pp. 510-521. |
Direitos |
Copyright 2015 Springer |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #080199 Artificial Intelligence and Image Processing not elsewhere classified #089999 Information and Computing Sciences not elsewhere classified #tagging data #tag-based item recommendation #discounted cumulative gain #top-N recommendation |
Tipo |
Book Chapter |