research-article

How Relevant is the Irrelevant Data: Leveraging the Tagging Data for a Learning-to-Rank Model

Authors:

Richi NayakAuthors Info & Claims

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

Pages 23 - 32

https://doi.org/10.1145/2835776.2835790

Published: 08 February 2016 Publication History

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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Cited By

Balasubramaniam TNayak RYuen C(2020)Efficient Nonnegative Tensor Factorization via Saturating Coordinate DescentACM Transactions on Knowledge Discovery from Data10.1145/338565414:4(1-28)Online publication date: 30-May-2020
https://dl.acm.org/doi/10.1145/3385654
Ifada NSophan MFitriantama MWahyuni S(2020)Collaborative Filtering Item Recommendation Methods based on Matrix Factorization and Clustering Approaches2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS)10.1109/EECCIS49483.2020.9263450(226-230)Online publication date: 26-Aug-2020
https://doi.org/10.1109/EECCIS49483.2020.9263450
Ifada NSophan MSyachrudin ISugiharto S(2019)An Efficient Scheme to Combine the User Demographics and Item Attribute for Solving Data Sparsity and Cold-start Problems2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)10.1109/ICICoS48119.2019.8982394(1-6)Online publication date: Oct-2019
https://doi.org/10.1109/ICICoS48119.2019.8982394
Show More Cited By

Index Terms

How Relevant is the Irrelevant Data: Leveraging the Tagging Data for a Learning-to-Rank Model
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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cover image ACM Conferences

WSDM '16: Proceedings of the Ninth ACM International Conference on Web Search and Data Mining

February 2016

746 pages

ISBN:9781450337168

DOI:10.1145/2835776

General Chairs:
Paul N. Bennett
Microsoft Research
,
Vanja Josifovski
Pinterest
,
Program Chairs:
Jennifer Neville
Purdue University
,
Filip Radlinski
Microsoft

Copyright © 2016 ACM.

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Published: 08 February 2016

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WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining

February 22 - 25, 2016

California, San Francisco, USA

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WSDM '16 Paper Acceptance Rate 67 of 368 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Balasubramaniam TNayak RYuen C(2020)Efficient Nonnegative Tensor Factorization via Saturating Coordinate DescentACM Transactions on Knowledge Discovery from Data10.1145/338565414:4(1-28)Online publication date: 30-May-2020
https://dl.acm.org/doi/10.1145/3385654
Ifada NSophan MFitriantama MWahyuni S(2020)Collaborative Filtering Item Recommendation Methods based on Matrix Factorization and Clustering Approaches2020 10th Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS)10.1109/EECCIS49483.2020.9263450(226-230)Online publication date: 26-Aug-2020
https://doi.org/10.1109/EECCIS49483.2020.9263450
Ifada NSophan MSyachrudin ISugiharto S(2019)An Efficient Scheme to Combine the User Demographics and Item Attribute for Solving Data Sparsity and Cold-start Problems2019 3rd International Conference on Informatics and Computational Sciences (ICICoS)10.1109/ICICoS48119.2019.8982394(1-6)Online publication date: Oct-2019
https://doi.org/10.1109/ICICoS48119.2019.8982394
Ifada NSyachrudin ISophan MWahyuni S(2019)Enhancing the Performance of Library Book Recommendation System by Employing the Probabilistic-Keyword Model on a Collaborative Filtering ApproachProcedia Computer Science10.1016/j.procs.2019.08.176157(345-352)Online publication date: 2019
https://doi.org/10.1016/j.procs.2019.08.176
Lin YWu JXu BXu KLin H(2017)Learning to rank using multiple loss functionsInternational Journal of Machine Learning and Cybernetics10.1007/s13042-017-0730-4Online publication date: 12-Oct-2017
https://doi.org/10.1007/s13042-017-0730-4
Rawashdeh MShorfuzzaman MArtoli AShamim Hossain MGhoneim A(2017)Mining tag-clouds to improve social media recommendationMultimedia Tools and Applications10.1007/s11042-016-4039-176:20(21157-21170)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s11042-016-4039-1

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