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A Novel Recommendation System for Next Feature in Software

  • Victor R. PrataEmail author
  • Ronaldo S. Moreira
  • Luan S. Cordeiro
  • Átilla N. Maia
  • Alan R. Martins
  • Davi A. Leão
  • C. H. L. Cavalcante
  • Amauri H. Souza Júnior
  • Ajalmar R. Rocha Neto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11871)

Abstract

Software that needs to fulfill many tasks requires a large number of components. Users of these software need a lot of time to find the desired functionality or follow a particular workflow. Recommendation systems can optimize a user’s working time by recommending the next features he/she needs. Given that, we evaluate the use of three algorithms (Markov Chain, IndRNN, and LSTM) commonly applied in sequence recommendation/classification in a dataset that reflects the use of the accounting software from Fortes Tecnologia. We analyze the results under two aspects: accuracy for top-5 recommendations and training time. The results show that the IndRNN achieved the highest accuracy, while the Markov Chain reached the lowest training time.

Keywords

Recommendation system Independently Recurrent Neural Network Sequential recommendation Deep learning Commercial applications 

Notes

Acknowledgments

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Ní­vel Superior – Brasil (CAPES) – Finance Code 001. The authors also thank the Fundação Cearense de Apoio ao Desenvolvimento Cientí­fico e Tecnológico (FUNCAP), for the financial support.

References

  1. 1.
    Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
  2. 2.
    Chollet, F., et al.: Keras (2015). https://keras.io
  3. 3.
    Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-642-24797-2CrossRefzbMATHGoogle Scholar
  4. 4.
    He, Q., et al.: Web query recommendation via sequential query prediction. In: IEEE 25th International Conference on Data Engineering, pp. 1443–1454 (2009)Google Scholar
  5. 5.
    Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. CoRR 1511.06939 (2015)Google Scholar
  6. 6.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  7. 7.
    Hosseinzadeh Aghdam, M., Hariri, N., Mobasher, B., Burke, R.: Adapting recommendations to contextual changes using hierarchical hidden Markov models. In: Proceedings of the 9th ACM Conference on Recommender Systems, RecSys 2015, pp. 241–244 (2015)Google Scholar
  8. 8.
    Jannach, D., Ludewig, M.: When recurrent neural networks meet the neighborhood for session-based recommendation. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, pp. 306–310 (2017)Google Scholar
  9. 9.
    Li, S., Li, W., Cook, C., Zhu, C., Gao, Y.: Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN. arXiv e-prints arXiv:1803.04831, March 2018
  10. 10.
    Quadrana, M., Cremonesi, P., Jannach, D.: Sequence-aware recommender systems. CoRR 1802.08452 (2018)Google Scholar
  11. 11.
    Shani, G., Heckerman, D., Brafman, R.I.: An MDP-based recommender system. J. Mach. Learn. Res. 6, 1265–1295 (2005)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Soh, H., Sanner, S., White, M., Jamieson, G.: Deep sequential recommendation for personalized adaptive user interfaces. In: Proceedings of the 22nd International Conference on Intelligent User Interfaces, pp. 13–16. ACM IUI (2017)Google Scholar
  13. 13.
    Usken, H.M., Stagge, P.: Recurrent neural networks for time series classification. Neurocomputing 50, 223–235 (2003)CrossRefGoogle Scholar
  14. 14.
    Zhang, Y., et al.: Sequential click prediction for sponsored search with recurrent neural networks. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, AAAI 2014, pp. 1369–1375 (2014)Google Scholar
  15. 15.
    Zheng, H.T., Chen, J.Y., Liang, N., Sangaiah, K.A., Jiang, Y., Zhao, C.Z.: A deep temporal neural music recommendation model utilizing music and user metadata. Appl. Sci. 9, 703 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Victor R. Prata
    • 1
    Email author
  • Ronaldo S. Moreira
    • 2
  • Luan S. Cordeiro
    • 1
  • Átilla N. Maia
    • 1
  • Alan R. Martins
    • 1
  • Davi A. Leão
    • 1
  • C. H. L. Cavalcante
    • 1
  • Amauri H. Souza Júnior
    • 1
  • Ajalmar R. Rocha Neto
    • 1
  1. 1.Federal Institute of CearáFortalezaBrazil
  2. 2.Fortes TecnologiaFortalezaBrazil

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