Do-Rank: DCG optimization for learning-to-rank in tag-based item recommendation systems

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.

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Abstract

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.

Impact and interest:

1 citations in Scopus
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2 citations in Web of Science®

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ID Code: 83203
Item Type: Book Chapter
Additional Information: Proceedings of 19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015 [Part II]
Keywords: tagging data, tag-based item recommendation, discounted cumulative gain, top-N recommendation
DOI: 10.1007/978-3-319-18032-8_40
ISBN: 9783319180328
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > OTHER INFORMATION AND COMPUTING SCIENCES (089900) > Information and Computing Sciences not elsewhere classified (089999)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Springer
Deposited On: 07 Apr 2015 23:56
Last Modified: 30 Nov 2015 13:18

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