Skip to main content
SpringerLink
Log in
Book cover

International Conference on Applications of Natural Language to Information Systems

NLDB 2019: Natural Language Processing and Information Systems pp 29–40Cite as

Learning Mobile App Embeddings Using Multi-task Neural Network

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11608))

Abstract

Last few years have seen a consistent increase in the availability and usage of mobile application (apps). Mobile operating systems have dedicated stores to host these apps and make them easily discoverable. Also, app developers depict their core features in textual descriptions while consumers share their opinions in form of user reviews. Apart from these inputs, applications hosted on app stores also contain indicators such as category, app ratings, and age ratings which affect the retrieval mechanisms and discoverability of these applications. An attempt is made in this paper to jointly model app descriptions and reviews to evaluate their use in predicting other indicators like app category and ratings. A multi-task neural architecture is proposed to learn and analyze the influence of application’s textual data to predict other categorical parameters. During the training process, the neural architecture also learns generic app-embeddings, which aid in other unsupervised tasks like nearest neighbor analysis and app clustering. Various qualitative and quantitative experiments are performed on these learned embeddings to achieve promising results.

A. Bajaj and S. Krishna—Equal contribution.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Statista: Number of available applications in the Google Play Store from December 2009 to December 2018. https://www.statista.com/statistics/266210/number-of-available-applications-in-the-google-play-store/

  2. Google: Play Store. https://play.google.com/store

  3. Zhu, H., Chen, E., Xiong, H., Cao, H., Tian, J.: Mobile app classification with enriched contextual information. IEEE Trans. Mob. Comput. 13, 1550–1563 (2014)

    Article  Google Scholar 

  4. Park, D.H., Liu, M., Zhai, C., Wang, H.: Leveraging user reviews to improve accuracy for mobile app retrieval. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 533–542. ACM (2015)

    Google Scholar 

  5. Park, D.H., Fang, Y., Liu, M., Zhai, C.: Mobile app retrieval for social media users via inference of implicit intent in social media text. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 959–968. ACM (2016)

    Google Scholar 

  6. Bajaj, A., Tiwari, H., Vala, V.: Enhanced learning to rank using cluster-loss adjustment. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 70–77. ACM (2019)

    Google Scholar 

  7. Radosavljevic, V., et al.: Smartphone app categorization for interest targeting in advertising marketplace. In: Proceedings of the 25th International Conference Companion on World Wide Web, International World Wide Web Conferences Steering Committee, pp. 93–94 (2016)

    Google Scholar 

  8. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  9. Rustgi, P., Fung, C., Rashidi, B., McInnes, B.: Droidvisor: an android secure application recommendation system. In: 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM), pp. 1071–1076. IEEE (2017)

    Google Scholar 

  10. Bajaj, A., Krishna, S., Rungta, M., Tiwari, H., Vala, V.: Relemb: A relevance-based application embedding for mobile app retrieval and categorization arXiv:1904.06672 [cs.IR] (2019). http://arxiv.org/abs/1904.06672

  11. Ruder, S.: Multi-Task Learning Objectives for Natural Language Processing. http://ruder.io/multi-task-learning-nlp

  12. Caruana, R.: Multitask learning. Mach. Learn. 28, 41–75 (1997)

    Article  Google Scholar 

  13. Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167. ACM (2008)

    Google Scholar 

  14. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  15. Deng, L., Hinton, G., Kingsbury, B.: New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8599–8603. IEEE (2013)

    Google Scholar 

  16. Biswas, A., Bhutani, M., Sanyal, S.: MRNet-Product2Vec: a multi-task recurrent neural network for product embeddings. In: Altun, Y., et al. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10536, pp. 153–165. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71273-4_13

    Chapter  Google Scholar 

  17. Luhn, H.P.: A statistical approach to mechanized encoding and searching of literary information. IBM J. Res. Dev. 1, 309–317 (1957)

    Article  MathSciNet  Google Scholar 

  18. Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28, 11–21 (1972)

    Article  Google Scholar 

  19. Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18, 613–620 (1975)

    Article  Google Scholar 

  20. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 807–814 (2010)

    Google Scholar 

  21. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  22. Bridle, J.S.: Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition. In: Soulie, F.F., Herault, J. (eds.) Neurocomputing, vol. 68, pp. 227–236. Springer, Heidelberg (1990). https://doi.org/10.1007/978-3-642-76153-9_28

  23. Chollet, F., et al.: Keras (2015). https://keras.io

  24. Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)

    Google Scholar 

  25. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Article  Google Scholar 

  26. Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. (2), 224–7 (1979)

    Google Scholar 

  27. Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Samsung R&D Institute, Bengaluru, India

    Ahsaas Bajaj, Shubham Krishna, Hemant Tiwari & Vanraj Vala

Authors
  1. Ahsaas Bajaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shubham Krishna
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hemant Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vanraj Vala
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahsaas Bajaj .

Editor information

Editors and Affiliations

  1. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

  2. University of Salford, Salford, UK

    Farid Meziane

  3. University of Salford, Salford, UK

    Sunil Vadera

  4. Oakland University, Rochester, MI, USA

    Vijayan Sugumaran

  5. CSE, University of Salford, Salford, UK

    Mohamad Saraee

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bajaj, A., Krishna, S., Tiwari, H., Vala, V. (2019). Learning Mobile App Embeddings Using Multi-task Neural Network. In: Métais, E., Meziane, F., Vadera, S., Sugumaran, V., Saraee, M. (eds) Natural Language Processing and Information Systems. NLDB 2019. Lecture Notes in Computer Science(), vol 11608. Springer, Cham. https://doi.org/10.1007/978-3-030-23281-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23281-8_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23280-1

  • Online ISBN: 978-3-030-23281-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation