Dynamic convolutional neural network for eliminating item sparse data on recommender system

(1) * Hanafi Hanafi Mail (Amikom University, Indonesia)
(2) Nanna Suryana Mail (Univeristi Teknikal Malaysia Melaka, Malaysia)
(3) Abdul Samad Hasan Basari Mail (Univeristi Teknikal Malaysia Melaka, Malaysia)
*corresponding author


Several efforts have been conducted to handle sparse product rating in e-commerce recommender system. One of them is the inclusion of texts such as product review, abstract, product description, and synopsis. Later, it converted to become rating value. Previous researches have tried to extract these texts based on bag of word and word order. However, this approach was given misunderstanding of text description of products. This research proposes a novel Dynamic Convolutional Neural Network (DCNN) to improve meaning accuracy of product review on a collaborative filtering recommender system. DCNN was used to eliminate item sparse data on text product review while the accuracy level was measured by Root Mean Squared Error (RMSE). The result shows that DCNN has outperformed the other previous methods.


Recommender system; E-commerce; Convolutional Deep learning; Collaborative filtering




Article metrics

Abstract views : 1889 | PDF views : 267




Full Text



[1] R. Gulati and J. Garino, “Get the right mix of bricks & clicks.,” Harv. Bus. Rev., vol. 78, no. 3, pp. 107–114, 2000, available at : https://europepmc.org/abstract/med/11183973.

[2] Hanafi, N. Suryana, and A. Sammad, “Evaluation of e-Service Quality, Perceived Value on Customer Satisfaction and Customer Loyalty: A Study in Indonesia,” Int. Bus. Manag., vol. 11, no. 11, pp. 1892–1900, 2017, doi: https://doi.org/10.3923/ibm.2017.1892.1900.

[3] C. A. Gomez-Uribe and N. Hunt, “The netflix recommender system: Algorithms, business value, and innovation,” ACM Trans. Manag. Inf. Syst., vol. 6, no. 4, p. 13, 2016, doi: https://doi.org/10.1145/2843948.

[4] J. Davidson et al., “The YouTube video recommendation system,” in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 293–296, doi: https://doi.org/10.1145/1864708.1864770.

[5] J. Ben Schafer, J. A. Konstan, and J. Riedl, “E-Commerce Recommendation Applications,” 2001, pp. 115–153, doi: https://doi.org/10.1007/978-1-4615-1627-9_6.

[6] Hanafi, N. Suryana, and A. Basari, “Deep Learning for Recommender System Based on Application Domain Classification Perspective: a Review,” J. Theor. Appl. Inf. Technol., vol. 96, no. 14, pp. 4513–4529, 2018, available at : http://www.jatit.org/volumes/Vol96No14/17Vol96No14.pdf.

[7] Hanafi, N. Suryana, and A. Basari, “An understanding and approach solution for cold start problem associated with recommender system : a literature review,” J. Theor. Appl. Inf. Technol., vol. 96, no. 9, 2018, available at : http://www.jatit.org/volumes/Vol96No9/26Vol96No9.pdf.

[8] F. Ricci, L. Rokach, and B. Shapira, Eds., Recommender Systems Handbook, 2015, doi: https://doi.org/10.1007/978-1-4899-7637-6.

[9] A. den Oord, S. Dieleman, and B. Schrauwen, “Deep content-based music recommendation,” in Advances in neural information processing systems, 2013, pp. 2643–2651, available at : http://papers.nips.cc/paper/5004-deep-content-based-.

[10] S. Jaradat, “Deep Cross-Domain Fashion Recommendation,” in Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys ’17, 2017, pp. 407–410, doi: https://doi.org/10.1145/3109859.3109861.

[11] J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Syst., vol. 46, pp. 109–132, Jul. 2013, doi: https://doi.org/10.1016/j.knosys.2013.03.012.

[12] E. Çano and M. Morisio, “Hybrid recommender systems: A systematic literature review,” Intell. Data Anal., vol. 21, no. 6, pp. 1487–1524, Nov. 2017, doi: https://doi.org/10.3233/IDA-163209.

[13] X. Wang and Y. Wang, “Improving Content-based and Hybrid Music Recommendation using Deep Learning,” in Proceedings of the ACM International Conference on Multimedia - MM ’14, 2014, pp. 627–636, doi: https://doi.org/10.1145/2647868.2654940.

[14] K. Park, J. Lee, and J. Choi, “Deep Neural Networks for News Recommendations,” in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management - CIKM ’17, 2017, pp. 2255–2258, doi: https://doi.org/10.1145/3132847.3133154.

[15] L. Chen, G. Chen, and F. Wang, “Recommender systems based on user reviews: the state of the art,” User Model. User-adapt. Interact., vol. 25, no. 2, pp. 99–154, 2015, doi: https://doi.org/10.1007/s11257-015-9155-5.

[16] L. Qu, G. Ifrim, and G. Weikum, “The bag-of-opinions method for review rating prediction from sparse text patterns,” in Proceedings of the 23rd International Conference on Computational Linguistics, 2010, pp. 913–921, available at : https://dl.acm.org/citation.cfm?id=1873884.

[17] G. Ling, M. R. Lyu, and I. King, “Ratings meet reviews, a combined approach to recommend,” in Proceedings of the 8th ACM Conference on Recommender systems - RecSys ’14, 2014, pp. 105–112, doi: https://doi.org/10.1145/2645710.2645728.

[18] J. McAuley and J. Leskovec, “Hidden factors and hidden topics,” in Proceedings of the 7th ACM conference on Recommender systems - RecSys ’13, 2013, pp. 165–172, doi: https://doi.org/10.1145/2507157.2507163.

[19] A. Severyn and A. Moschitti, “Twitter Sentiment Analysis with Deep Convolutional Neural Networks,” in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’15, 2015, pp. 959–962, doi: https://doi.org/10.1145/2766462.2767830.

[20] J. Wang, “Predicting Yelp Star Ratings Based on Text Analysis of User Reviews,” 2015, available at: https://pdfs.semanticscholar.org/1445/e46d8bf48f5739246c290340fbb15113902e.pdf.

[21] 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, 2015, pp. 1235–1244, doi: https://doi.org/10.1145/2783258.2783273.

[22] J. Gao, P. Pantel, M. Gamon, X. He, and L. Deng, “Modeling Interestingness with Deep Neural Networks,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 2–13, doi: https://doi.org/10.3115/v1/D14-1002.

[23] Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil, “A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval,” in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM ’14, 2014, pp. 101–110, doi: https://doi.org/10.1145/2661829.2661935.

[24] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324, 1998, doi: https://doi.org/10.1109/5.726791.

[25] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, “Natural language processing (almost) from scratch,” J. Mach. Learn. Res., vol. 12, no. Aug, pp. 2493–2537, 2011, available at : http://www.jmlr.org/papers/v12/collobert11a.html.

[26] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, “A Convolutional Neural Network for Modelling Sentences,” in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014, pp. 655–665, doi: https://doi.org/10.3115/v1/P14-1062.

[27] Y. Kim, “Convolutional neural networks for sentence classification,” arXiv Prepr. arXiv1408.5882, 2014, available at : https://arxiv.org/abs/1408.5882.

[28] K. Iwahama, Y. Hijikata, and S. Nishida, “Content-based filtering system for music data,” in 2004 International Symposium on Applications and the Internet Workshops. 2004 Workshops., 2004, pp. 480–487, doi: https://doi.org/10.1109/SAINTW.2004.1268677.

[29] 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 - RecSys ’16, 2016, pp. 233–240, doi: https://doi.org/10.1145/2959100.2959165.

[30] A. Gunawardana and G. Shani, “A survey of accuracy evaluation metrics of recommendation tasks,” J. Mach. Learn. Res., vol. 10, no. Dec, pp. 2935–2962, 2009, available at : http://www.jmlr.org/papers/v10/gunawardana09a.html.

[31] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer (Long. Beach. Calif)., no. 8, pp. 30–37, 2009, doi: https://doi.org/10.1109/MC.2009.263.

[32] F. M. Harper and J. A. Konstan, “The movielens datasets: History and context,” Acm Trans. Interact. Intell. Syst., vol. 5, no. 4, p. 19, 2016, doi: https://doi.org/10.1145/2827872.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0