Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
권호 표지
DOI QR코드

DOI QR Code

Modeling of Convolutional Neural Network-based Recommendation System

  • Kim, Tae-Yeun (National Program of Excellence in Software center, Chosun University)
  • Received : 2021.12.03
  • Accepted : 2021.12.29
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Collaborative filtering is one of the commonly used methods in the web recommendation system. Numerous researches on the collaborative filtering proposed the numbers of measures for enhancing the accuracy. This study suggests the movie recommendation system applied with Word2Vec and ensemble convolutional neural networks. First, user sentences and movie sentences are made from the user, movie, and rating information. Then, the user sentences and movie sentences are input into Word2Vec to figure out the user vector and movie vector. The user vector is input on the user convolutional model while the movie vector is input on the movie convolutional model. These user and movie convolutional models are connected to the fully-connected neural network model. Ultimately, the output layer of the fully-connected neural network model outputs the forecasts for user, movie, and rating. The test result showed that the system proposed in this study showed higher accuracy than the conventional cooperative filtering system and Word2Vec and deep neural network-based system suggested in the similar researches. The Word2Vec and deep neural network-based recommendation system is expected to help in enhancing the satisfaction while considering about the characteristics of users.

Keywords

References

  1. Lee, Y., Won, H., Shim, J., and Ahn, H., "A Hybrid Collaborative Filtering-based Product Recommender System using Search Keywords", Journal of Intelligence and Information Systems, vol. 26, no. 1, pp. 151-166, 2020. https://doi.org/10.13088/jiis.2020.26.1.151
  2. Cho, S. E., and Lim, H. S., "A Study on Product Recommendation System Based on User Search keyword", Journal of Digital Contents Society, vol. 20, no. 2, pp. 315-320, 2019. https://doi.org/10.9728/dcs.2019.20.2.315
  3. Park, S. J., Kim, Y. M., and Ahn, J. J., "Development of product recommender system using collaborative filtering and stacking model," Journal of Convergence for Information Technology, vol. 9, no. 6, pp. 83-90, 2019. https://doi.org/10.22156/CS4SMB.2019.9.6.083
  4. Turner, C. A., Jacobs, A. D., Marques, C. K., Oates, J. C., Kamen, D. L., Anderson, P. E., and Obeid, J. S., "Word2Vec inversion and traditional text classifiers for phenotyping lupus," BMC medical informatics and decision making, vol. 17, no. 1, pp. 1- 11. 2017. https://doi.org/10.1186/s12911-016-0389-x
  5. Hu, K., Wu, H., Qi, K., Yu, J., Yang, S., Yu, T., Zheng, J., and Liu, B., "A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model", Scientometrics, vol. 114, no. 3, pp. 1031-1068, 2017.
  6. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J., "Distributed representations of words and phrases and their compositionality", In Advances in neural information processing systems, pp. 3111-3119, 2013.
  7. Park, S., J., and Byun, Y., C., "Improving Recommendation Accuracy based on Machine Learning using Multi-Dimensional Features of Word2Vec", Journal of Advanced Information Technology and Convergence, vol. 19, no. 3, pp. 9-14, 2021.
  8. Zhao, Z., and Chen, X., "Deep Reinforcement Learning based Recommend System using stratified sampling", In IOP Conference Series: Materials Science and Engineering, vol. 466, no. 1, pp. 012110, 2018.
  9. Devooght, R., and Bersini, H., "Collaborative filtering with recurrent neural networks", arXiv preprint arXiv:1608.07400, 2016.
  10. Liu, S., Ji, H., and Wang, M. C., "Nonpooling convolutional neural network forecasting for seasonal time series with trends", IEEE transactions on neural networks and learning systems, vol. 31, no. 8, pp. 2879-2888, 2019. https://doi.org/10.1109/tnnls.2019.2934110
  11. Xu, W., Huang, H., Zhang, J., Gu, H., Yang, J., and Gui, G., "CNN-based skip-gram method for improving classification accuracy of chinese text", KSII Transactions on Internet and Information Systems (TIIS), vol. 13, no. 12, pp. 6080-6096, 2019. https://doi.org/10.3837/tiis.2019.12.016
  12. Fong, C. M., Wang, H. W., Kuo, C. H., and Hsieh, P. C., "Image quality assessment for advertising applications based on neural network", Journal of Visual Communication and Image Representation, vol. 63, pp. 102593, 2019. https://doi.org/10.1016/j.jvcir.2019.102593