skip to main content
10.1145/2766462.2767785acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
short-paper

Adaptive User Engagement Evaluation via Multi-task Learning

Published:09 August 2015Publication History

ABSTRACT

User engagement evaluation task in social networks has recently attracted considerable attention due to its applications in recommender systems. In this task, the posts containing users' opinions about items, e.g., the tweets containing the users' ratings about movies in the IMDb website, are studied. In this paper, we try to make use of tweets from different web applications to improve the user engagement evaluation performance. To this aim, we propose an adaptive method based on multi-task learning. Since in this paper we study the problem of detecting tweets with positive engagement which is a highly imbalanced classification problem, we modify the loss function of multi-task learning algorithms to cope with the imbalanced data. Our evaluations over a dataset including the tweets of four diverse and popular data sources, i.e., IMDb, YouTube, Goodreads, and Pandora, demonstrate the effectiveness of the proposed method. Our findings suggest that transferring knowledge between data sources can improve the user engagement evaluation performance.

References

  1. R. Akbani, S. Kwek, and N. Japkowicz. Applying support vector machines to imbalanced datasets. In ECML, pages 39--50, 2004.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. J. G. C. de Souza, H. Zamani, M. Negri, M. Turchi, and D. Falavigna. Multitask learning for adaptive quality estimation of automatically transcribed utterances. In NAACL-HLT, pages 714--724, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  3. R. Caruana. Multitask learning. Machine Learning, 28(1):41--75, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Chen, J. Liu, and J. Ye. Learning incoherent sparse and low-rank patterns from multiple tasks. ACM Trans. Knowl. Discov. Data, 5(4):22:1--22:31, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Chen, L. Tang, J. Liu, and J. Ye. A convex formulation for learning shared structures from multiple tasks. In ICML, pages 137--144, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. S. Dooms, T. De Pessemier, and L. Martens. Mining cross-domain rating datasets from structured data on twitter. In MSM@WWW, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D. Loiacono, A. Lommatzsch, and R. Turrin. An analysis of the 2014 recsys challenge. In RecSys Challenge, pages 1--6, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. S. Petrovic, M. Osborne, and V. Lavrenko. Rt to win! predicting message propagation in twitter. In ICWSM, pages 586--589, 2011.Google ScholarGoogle Scholar
  9. D. Powers. Evaluation: From precision, recall and f-measure to roc, informedness, markedness & correlation. J. Mach. Learn. Tech., 2(1):37--63, 2011.Google ScholarGoogle ScholarCross RefCross Ref
  10. I. Uysal and W. B. Croft. User oriented tweet ranking: A filtering approach to microblogs. In CIKM, pages 2261--2264, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. H. Zamani, A. Shakery, and P. Moradi. Regression and learning to rank aggregation for user engagement evaluation. In RecSys Challenge, pages 29--34, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Adaptive User Engagement Evaluation via Multi-task Learning

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval
        August 2015
        1198 pages
        ISBN:9781450336215
        DOI:10.1145/2766462

        Copyright © 2015 ACM

        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 [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 9 August 2015

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • short-paper

        Acceptance Rates

        SIGIR '15 Paper Acceptance Rate70of351submissions,20%Overall Acceptance Rate792of3,983submissions,20%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader