Skip to main content

Deep Autoencoder on Personalized Facet Selection

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1142))

Included in the following conference series:

Abstract

Information overloading leads to the need for an efficient search tool to eliminate a considerable amount of irrelevant or unimportant data and present the contents in an easy-browsing form. Personalized faceted search has been one of the potential tools to provide a hierarchical list of facets or categories that helps searchers to organize the information of the search results. Facet selection is one of the important steps to pursue a good faceted search. Collaborative-based personalization was introduced to facet selection. Previous studies have been performed on the use of Collaborative Filtering techniques for personalized facet selection. However, none of the study has investigated Artificial neural network techniques on personalized facet selection. Therefore, this study aims to investigate the possible use of deep Autoencoder on the prediction of facet interests. Autoencoder model was applied to address the association of collaborative interest in facets. The experiments were conducted on 100K and 1M rating records of Movielen dataset. Rating score was used to represent the explicit feedback on facet interests. The performance was reported by comparing the proposed technique and the state-of-the-art model-based Collaborative Filtering techniques in terms of prediction accuracy and computational time. The results showed that the proposed Autoencoder-based model achieved better performance and it was able to significantly improve the prediction of personal facet interests.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://surpriselib.com/.

References

  1. Basu Roy, S., Wang, H., Das, G., Nambiar, U., Mohania, M.: Minimum-effort driven dynamic faceted search in structured databases. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 13–22. ACM (2008)

    Google Scholar 

  2. Chantamunee, S., Fung, C.C., Wong, K.W., Dumkeaw, C.: Knowledge discovery from thai research articles by solr-based faceted search. In: Unger, H., Sodsee, S., Meesad, P. (eds.) IC2IT 2018. AISC, vol. 769, pp. 337–346. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-93692-5_33

    Chapter  Google Scholar 

  3. Chantamunee, S., Wong, K.W., Fung, C.C.: Collaborative filtering for personalised facet selection. In: Proceedings of the 10th International Conference on Advances in Information Technology, p. 15. ACM (2018)

    Google Scholar 

  4. Harper, F.M., Konstan, J.A.: The movielens datasets: History and context. ACM Trans. Interact. Intell. Syst. (TiiS) 5(4), 19 (2016)

    Google Scholar 

  5. Kim, H.J., Zhu, Y., Kim, W., Sun, T.: Dynamic faceted navigation in decision making using semantic web technology. Decis. Support. Syst. 61, 59–68 (2014)

    Article  Google Scholar 

  6. Koren, J., Zhang, Y., Liu, X.: Personalized interactive faceted search. In: Proceedings of the 17th International Conference on WWW, pp. 477–486. ACM (2008)

    Google Scholar 

  7. Kuchaiev, O., Ginsburg, B.: Training deep autoencoders for collaborative filtering. arXiv preprint arXiv:1708.01715 (2017)

  8. Le, T., Vo, B., Duong, T.H.: Personalized facets for semantic search using linked open data with social networks. In: The 3rd International Conference on Innovations in Bio-Inspired Computing and Applications, pp. 312–317. IEEE (2012)

    Google Scholar 

  9. Liberman, S., Lempel, R.: Approximately optimal facet value selection. Sci. Comput. Program. 94, 18–31 (2014)

    Article  Google Scholar 

  10. Liu, H., Wu, Z., Zhang, X.: CPLR: collaborative pairwise learning to rank for personalized recommendation. Knowl. Based Syst. 148, 31–40 (2018)

    Article  Google Scholar 

  11. Liu, Y., Wang, S., Khan, M.S., He, J.: A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Min. Anal. 1(3), 211–221 (2018)

    Article  Google Scholar 

  12. Momeni, E., Braendle, S., Adar, E.: Adaptive faceted ranking for social media comments. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 789–792. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16354-3_86

    Chapter  Google Scholar 

  13. Nazi, A., Asudeh, A., Das, G., Zhang, N., Jaoua, A.: MobiFace: a mobile application for faceted search over hidden web databases. In: International Conference on Computer and Applications, pp. 13–17. IEEE (2017)

    Google Scholar 

  14. Niu, X., Fan, X., Zhang, T.: Understanding faceted search from data science and human factor perspectives. ACM Trans. Inf. Syst. 37(2), 14:1–14:27 (2019)

    Article  Google Scholar 

  15. Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autoencoders meet collaborative filtering. In: Proceedings of 24th International Conference on WWW, pp. 111–112. ACM (2015)

    Google Scholar 

  16. Silveira, T., Zhang, M., Lin, X., Liu, Y., Ma, S.: How good your recommender system is? A survey on evaluations in recommendation. Inter. J. Mach. Learn. Cybern., 1–19 (2017)

    Google Scholar 

  17. Tran, D., et al.: Deep autoencoder for recommender systems: parameter influence analysis. CoRR (2019)

    Google Scholar 

  18. Vandic, D., Aanen, S., Frasincar, F., Kaymak, U.: Dynamic facet ordering for faceted product search engines. IEEE Trans. Knowl. Data Eng. 29(5), 1004–1016 (2017)

    Article  Google Scholar 

  19. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: a survey and new perspectives. ACM Comput. Surv. (CSUR) 52(1), 5 (2019)

    Google Scholar 

  20. Zheng, B., Zhang, W., Feng, X.F.B.: A survey of faceted search. J. Web Eng. 12(1&2), 041–064 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Siripinyo Chantamunee .

Editor information

Editors and Affiliations

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

Chantamunee, S., Wong, K.W., Fung, C.C. (2019). Deep Autoencoder on Personalized Facet Selection. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36808-1_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics