Browse > Article
http://dx.doi.org/10.6109/jkiice.2019.23.3.237

A Recommendation Model based on Character-level Deep Convolution Neural Network  

Ji, JiaQi (Department of Information Center, Hebei Normal University for Nationalities)
Chung, Yeongjee (Department of Computer and Software Engineering, Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.3, 2019 , pp. 237-246 More about this Journal
Abstract
In order to improve the accuracy of the rating prediction of the recommendation model, not only user-item rating data are used but also consider auxiliary information of item such as comments, tags, or descriptions. The traditional approaches use a word-level model of the bag-of-words for the auxiliary information. This model, however, cannot utilize the auxiliary information effectively, which leads to shallow understanding of auxiliary information. Convolution neural network (CNN) can capture and extract feature vector from auxiliary information effectively. Thus, this paper proposes character-level deep-Convolution Neural Network based matrix factorization (Char-DCNN-MF) that integrates deep CNN into matrix factorization for a novel recommendation model. Char-DCNN-MF can deeper understand auxiliary information and further enhance recommendation performance. Experiments are performed on three different real data sets, and the results show that Char-DCNN-MF performs significantly better than other comparative models.
Keywords
Recommendation model; Convolution neural network; Matrix factorization; Deep Learning;
Citations & Related Records
연도 인용수 순위
  • Reference
1 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Computer Vision and Pattern Recognition (cs.CV) arXiv preprint arXiv:1506.02640v5, 2016 [Online]. Available: https://arXiv.org/abs/1506.02640v5.
2 K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Computer Vision and Pattern Recognition (cs.CV) arXiv preprint arXiv:1512.03385v1, 2015 [Online]. Available: https://arXiv.org/abs/1512.03385.
3 Y. Zhang, W. Chan, and N. Jaitly, "Very deep convolutional networks for end-to-end speech recognition," in Proceeding of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA: IEEE Signal Processing Society, pp. 4845-4849, Mar. 2017.
4 Y. Zhang, M. Pezeshki, P Brakel, S. Zhang, C. L. Y. Bengio, and A. Courville, "Towards end-to-end speech recognition with deep convolutional neural networks," Computation and Language (cs.CL) arXiv preprint arXiv:1701.02720, 2017 [Online]. Available: https://arXiv.org/abs/1701.02720.
5 M. Guignard, M. Schild, C. S. Bederian, N. Wolovick, and A. J. Vega, "Performance Characterization of State-Of-The-Art Deep Learning Workloads on an IBM "Minsky" Platform," in Proceedings of the 51st Hawaii International Conference on System Sciences(HICSS-51), Waikoloa Village, HI: HICSS, pp. 5619-5626, Jan. 2018.
6 M. Abbes, Z. Kechaou, and A. M. Alimi, "Enhanced Deep Learning Models for Sentiment Analysis in Arab Social Media," Neural Information Processing. ICONIP 2017 (International Conference on Neural Information Processing). LNCS, vol. 10638. Springer, Cham, pp. 667-676, 2017.
7 Y. Kim, "Convolutional Neural Networks for Sentence Classification," Computation and Language (cs.CL) arXiv preprint arXiv:1408.5882, 2014 [Online]. Available: https://arXiv.org/abs/1408.5882.
8 B. Mitra and N. Craswell, "Neural Text Embeddings for Information Retrieval," in Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. Cambridge, UK: ACM New York, NY, pp. 813-814, Feb. 2017.
9 N. Craswell, W. B. Croft, J. Guo, M. de Rijke, and B. Mitra, "Report on the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR)," in SIGIR Forum, Workshop Report vol. 50, no. 2, pp. 96-103, Dec. 2016.
10 M. Quadrana, A. Karatzoglou, B. Hidasi, and P. Cremonesi, "Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks," Machine Learning (cs.LG) arXiv preprint arXiv:1706.04148v5, 2017 [Online]. Available: https://arXiv.org/abs/1706.04148.
11 D. Pechyony and V. Vapnik, "On the theory of learning with Privileged Information," in Proceedings of the 23rd International Conference on Neural Information Processing Systems. Vancouver, BC, vol. 2, pp. 1894-1902, Dec. 2010.
12 Y. Wu, C. DuBois, A. A. Zheng, and M. Ester, "Collaborative Denoising Auto-Encoders for Top-N Recommender Systems," in Proceedings of the Ninth ACM International Conference on Web Search and Data Mining. San Francisco, CA: ACM New York, NY, pp. 153-162, Feb. 2016.
13 C. Wang, and D.M. Blei, "Collaborative Topic Modeling for Recommending Scientific Articles," in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego, CA: ACM New York, NY, pp. 448-456, Aug. 2011.
14 H. Wang, N. Wang, and D.-Y. Yeung, "Collaborative Deep Learning for Recommender Systems," in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Sydney, NSW, Australia: ACM New York, NY, pp. 1235-1244, Aug. 2015.
15 D. Kim, C. Park, J. Oh, S. Lee, and H. Yu, "Convolutional Matrix Factorization for Document Context-Aware Recommendation," in Proceedings of the 10th ACM Conference on Recommender Systems. Boston, MA: ACM New York, NY, pp. 233-240, Sep. 2016.
16 Y. Koren, "Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model," in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, NV: ACM New York, NY, pp. 426-434, Aug. 2008.
17 R. Salakhutdinov and A. Mnih, "Probabilistic Matrix Factorization," in Proceedings of the 20th International Conference on Neural Information Processing Systems. Vancouver, BC, pp. 1257-1264, Dec. 2007.
18 N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salankhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
19 D. P. Kingma, and J. Ba, "Adam: A Method for Stochastic Optimization," Machine Learning (cs.LG) arXiv preprint arXiv:1412.6980v9, 2017 [Online]. Available: https://arXiv.org/abs/1412.698.