How relevant is the irrelevant data: Leveraging the tagging data for a learning-to-rank model

Ifada, Noor & Nayak, Richi (2016) How relevant is the irrelevant data: Leveraging the tagging data for a learning-to-rank model. In 9th ACM International Conference on Web Search and Data Mining, 22-25 February 2016, San Francisco, CA.

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For the task of tag-based item recommendations, the underlying tensor model faces several challenges such as high data sparsity and inferring latent factors effectively. To overcome the inherent sparsity issue of tensor models, we propose the graded-relevance interpretation scheme that leverages the tagging data effectively. Unlike the existing schemes, the graded-relevance scheme interprets the tagging data richly, differentiates the non-observed tagging data insightfully, and annotates each entry as one of the “relevant”, “likely relevant”, “irrelevant”, or “indecisive” labels. To infer the latent factors of tensor models correctly to produce the high quality recommendation, we develop a novel learning-to-rank method, Go-Rank, that optimizes Graded Average Precision (GAP). Evaluating the proposed method on real-world datasets, we show that the proposed interpretation scheme produces a denser tensor model by revealing “relevant” entries from the previously assumed “irrelevant” entries. Optimizing GAP as the ranking metric, the quality of the recommendations generated by Go-Rank is found superior against the benchmarking methods.

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ID Code: 97531
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Item recommendation, Tagging data, Graded-relevance scheme, Graded average precision
DOI: 10.1145/2835776.2835790
ISBN: 9781450337168
Subjects: 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 2016 ACM
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Deposited On: 19 Jul 2016 22:22
Last Modified: 23 Jul 2016 07:26

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