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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Abstract

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
Copyright Statement: Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org.
Deposited On: 19 Jul 2016 22:22
Last Modified: 23 Jul 2016 07:26

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