Skip to main content
SpringerLink
Log in
Book cover

The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

ICNC-FSKD 2019: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery pp 970–979Cite as

Construction of the HMM Intelligent Recommendation Model and Its Application in the Film Ticketing System

  • Conference paper
  • First Online:

  • 1266 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1075))

Abstract

A new intelligent recommendation method based on HMM is proposed in this paper, which can predict the future behavior of other users by training the known historical behavior data set of some users, so as to achieve the purpose of the intelligent recommendation. Firstly, the basic framework of an Intelligent Recommendation Model is established from the following aspects: the Representation of Observation Sequence, K-Means Clustering, HMM Training and HMM Nearest Neighbor Filtering. After that, the new model is applied to the Movie Ticketing System, which focuses on the Observation Sequence Representation of intelligent recommendation information in the HMM. Finally, basing on the MovieLens Data Set, the experiments of HMM Training and HMM Intelligent Recommendation in the Intelligent Recommendation Model are carried out. In the HMM Training experiment, the operation speed of the HMM Training is faster, and the HMM Models obtained by the HMM training is better. The results of the HMM Intelligent Recommendation experiments showed that the accuracy of the intelligent recommendation is 90.2%, meanwhile, the intelligent recommendation’s function of recommending movies for users and finding users for movies is realized. Therefore, the new Intelligent Recommendation Method based on HMM is effective and feasible, and this method is worth popularizing.

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   219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   279.99
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. Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.; Recommender Systems in An introduction. Cambridge University Press, New York (2010)

    Google Scholar 

  2. Osman, N.A., Noah, S.A.M.: Sentiment-based model for recommender systems. In: 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), pp. 1–6, Kota Kinabalu (2018)

    Google Scholar 

  3. Chen, W., Paik, I., Tanaka, T., Kumara, B.T.G.S.: Awareness of social influence for service recommendation. In: 2013 IEEE International Conference on Services Computing, pp. 767–768, Santa Clara (2013)

    Google Scholar 

  4. Boutsis, I., Karanikolaou, S., Kalogeraki, V.: Personalized event recommendations using social networks. In: 2015 16th IEEE International Conference on Mobile Data Management, pp. 84–93, Pittsburgh (2015)

    Google Scholar 

  5. Nguyen, M.N.H., Pham, C., Son, J., Hong, C.S.: Online learning-based clustering approach for news recommendation systems. In: 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS), pp. 1–4, Kanazawa (2016)

    Google Scholar 

  6. Khalil, K.M., Adnan, C.: Arabic speech synthesis based on HMM. In: 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 1091–1095, Hammamet (2018)

    Google Scholar 

  7. Juneja, K., Gill, N.S.: A PCT/PST improved HMM-PCA model for pose robust facial recognition. In: (2015) International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 131–136, Davangere (2015)

    Google Scholar 

  8. Ranjan, R., Dubey, R.K.: Isolated word recognition using HMM for Maithili dialect. In: 2016 International Conference on Signal Processing and Communication (ICSC), pp. 323–327, Noida (2016)

    Google Scholar 

  9. Zhang, X., Wang, J., Wang, X., Ma, X.: Improvement of dynamic hand gesture recognition based on HMM algorithm. In: 2016 International Conference on Information System and Artificial Intelligence (ISAI), pp. 401–406, Hong Kong (2016)

    Google Scholar 

  10. Movielens—GroupLens. https://grouplens.org/datasets/movielens/. Accessed 11 Feb 2019

Download references

Acknowledgement

This work has been supported by the Hunan Provincial Social Science Achievement Evaluation Committee (No. XSP19YBZ111), the Natural Science Foundation of Hunan province (No. 2017JJ2241), the Industry-University-Research Innovation Fund of Ministry of Education (No. 2018A02014) and the Scientific Research Project of Xiangnan University (No. 2018XJ24).

Author information

Authors and Affiliations

  1. College of Software and Communication Engineering, Xiangnan University, Chenzhou, 423000, Hunan, China

    Ruhua Lu

  2. College of Mathematics and Finance, Xiangnan University, Chenzhou, 423000, Hunan, China

    Jialu Zhang

Authors
  1. Ruhua Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jialu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jialu Zhang .

Editor information

Editors and Affiliations

  1. The University of Aizu, Aizuwakamatsu, Japan

    Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lu, R., Zhang, J. (2020). Construction of the HMM Intelligent Recommendation Model and Its Application in the Film Ticketing System. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_106

Download citation

Publish with us

Policies and ethics

Search

Navigation