Increase Robustness SDAE with Imputing Missing Value To Eliminate Users Sparse Data in Case E-Commerce Recommender System


  • Hanafi .
  • Nanna Suryana
  • Abd Samad Hasan Basari





recommender system, collaborative, deep SDAE, deep learning, sparse data, users sparse


Online shopping needs a computer machine to serve product information sale for customer or buyer candidate. Relevant information served by ecommerce system famous called recommender system. The successful to applied, it will have impact to increase of marketing target achievement. The character of information served by recommender system have to be special, personalized, relevant and fit according customer profiling. There are four kind of recommender system model, however there is one model that was successful to be applied in real ecommerce industry that popular named collaborative filtering. Collaborative filtering approach need a record users or customers activity in the past to generate recommendation for example rating record, purchasing record, testimony about product.  The majority collaborative filtering approaches rely on rating as fundamental computation to calculate product recommendation. However, just a little number of consumers who willing give rating for products less than a percent, according to several convince datasets such MovieLens. This problem causes of sparse product rating that will impact to product recommendation accuracy level. Sometime, in extreme condition, it is impossible to generate product recommendation. Several efforts have been conducting to handle product sparse rating, however they fail to generate product recommendation accurately when face extreme sparse data, such as matrix factorization family include SVD, NMF, SVD++. This research aims to develop a model to handle users sparse rating involving deep SDAE. One of the efforts to produce better output in handling this data sparse, our strategy is to imputing missing value by statistical method so that the input in SDAE is closer to the feasibility of data that is not too sparse. According to our experiment involve deep learning, TensorFlow, MovieLens datasets, evaluation method by root mean square error (RMSE), our approach involves reducing input missing value could address users sparse rating and increase robustness over several existing approach.




[1] G. R and G. J, “Get the right mix of bricks and clicks,†Harv. Bus. Rev., 2000.

[2] Hanafi, N. Suryana, and A. Sammad, “Evaluation of e-Service Quality, Perceived Value on Customer Satisfaction and Customer Loyalty: A Study in Indonesia.†Medwell Journal, pp. 1892–1900, 2017.

[3] C. A. Gomez-uribe and N. Hunt, “The Netflix Recommender System : Algorithms , Business Value ,†vol. 6, no. 4, 2015.

[4] J. Davidson and B. Liebald, “The YouTube video recommendation system The YouTube Video Recommendation System,†no. August, 2014.

[5] J. Ben Schafer, J. Konstan, and J. Riedl, “E-commerce recommendation applications,†Appl. Data Min. to Electron. …, pp. 115–153, 2001.


[7] L. Candillier, K. Jack, F. Fessant, and F. Meyer, Recommender systems, vol. 40, no. 3. 1997.

[8] E. Çano and M. Morisio, “Hybrid recommender systems: A systematic literature review,†Intell. Data Anal., vol. 21, no. 6, pp. 1487–1524, 2017.

[9] F. M. Harper and J. A. Konstan, “The MovieLens Datasets: History and Context,†ACM Trans. Interact. Intell. Syst., vol. 5, no. 4, p. 19:1--19:19, 2015.

[10] X. Wang and Y. Wang, “Improving Content-based and Hybrid Music Recommendation using Deep Learning,†pp. 627–636.

[11] S. Jaradat, “Deep Cross-Domain Fashion Recommendation,†Proc. Elev. ACM Conf. Recomm. Syst. - RecSys ’17, pp. 407–410, 2017.

[12] K. Park, J. Lee, and J. Choi, “Deep Neural Networks for News Recommendations,†CIKM’17, Novemb. 6-10, 2017, Singapore, 2017.

[13] P. Covington, J. Adams, and E. Sargin, “Deep Neural Networks for YouTube Recommendations,†Proc. 10th ACM Conf. Recomm. Syst. - RecSys ’16, pp. 191–198, 2016.

[14] Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,†pp. 42–49, 2009.

[15] A. van den Oord, S. Dieleman, and B. Schrauwen, “Deep Content-Based Music Recommendation,†pp. 2643–2651, 2013.

[16] Hanafi, N. Suryana, and abd samad hasan Basari, “HYBRIDIZATION APPROACH TO ELIMINATE SPARSE DATA BASED ON NONNEGATIVE MATRIX FACTORIZATION & DEEP LEARNING,†J. Theor. Appl. Inf. Technol., vol. 96, no. 14, pp. 4502–4512, 2018.

[17] R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization.,†Proc. Adv. Neural Inf. Process. Syst. 20 (NIPS 07), pp. 1257–1264, 2007.

[18] C. Wang and D. M. Blei, “Collaborative topic modeling for recommending scientific articles,†Proc. 17th ACM SIGKDD Int. Conf. Knowl. Discov. data Min. - KDD ’11, p. 448, 2011.

[19] S. Zhang, W. Wang, J. Ford, and F. Makedon, “Learning from Incomplete Ratings Using Non-negative Matrix Factorization,†Proc. 2006 SIAM Int. Conf. Data Min., pp. 549–553, 2006.

[20] H. Wang and D. Yeung, “Collaborative Deep Learning for Recommender Systems arXiv : 1409 . 2944v1 [ cs . LG ] 10 Sep 2014,†no. July 2015, 2014.

[21] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems,†Am. Lab., vol. 37, no. 10, p. 4, 2005.

[22] U. Ocepek, J. Rugelj, and Z. Bosni??, “Improving matrix factorization recommendations for examples in cold start,†Expert Syst. Appl., vol. 42, no. 19, pp. 6784–6794, 2015.

[23] C. C. Chen, Y. H. Wan, M. C. Chung, and Y. C. Sun, “An effective recommendation method for cold start new users using trust and distrust networks,†Inf. Sci. (Ny)., vol. 224, pp. 19–36, 2013.

[24] A. Gunawardana, “A Survey of Accuracy Evaluation Metrics of Recommendation Tasks,†vol. 10, pp. 2935–2962, 2009.

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How to Cite

., H., Suryana, N., & Samad Hasan Basari, A. (2018). Increase Robustness SDAE with Imputing Missing Value To Eliminate Users Sparse Data in Case E-Commerce Recommender System. International Journal of Engineering & Technology, 7(4.44), 137–142.
Received 2019-02-02
Accepted 2019-02-02
Published 2018-12-01