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 \( \left\{ {negative,null,positive} \right\} \), 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.
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Ifada, N., Nayak, R. (2015). Do-Rank: DCG Optimization for Learning-to-Rank in Tag-Based Item Recommendation Systems. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_40
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DOI: https://doi.org/10.1007/978-3-319-18032-8_40
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