Improved Collaborative Filtering Recommender System Based on Missing Values Imputation on E-Commerce

  • Kadek Abi Satria A V P Telkom University, Bandung, Indonesia
  • Z K A Baizal * Mail Telkom University, Bandung, Indonesia
  • (*) Corresponding Author
Keywords: Recommender System; Collaborative Filtering; E-commerce


One of the important aspects in e-commerce is how to recommend a product to users accurately. To achieve this goal, many e-commerce starts to build and research about recommender system. Many methods can be used to build a recommender system, one of them is using the collaborative filtering technique. This technique often experiences data sparsity problem that can impact to the recommender system prediction accuracy. To solve this problem, we apply improved collaborative filtering. This method predicts the missing values in the user item rating matrix. First, we do an initial selection to determine potential users who have the same characteristics with the active user. After that, we calculate the average distance between the active user and the other selected user. Next, we calculate missing values prediction. Missing values predictions is only done for items that have never been rated by other’s selected user but has been rated by the active user. We used Amazon electronic product with high sparsity level in this research to simulate the actual condition of e-commerce. We used MAE and RMSE to measure prediction accuracy. The methods we apply succeeds to improve the prediction accuracy compare to the conventional collaborative filtering method. The average MAE for method that we apply is 0.78 and RMSE 1.07


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

A. Hidayatullah and M. A. Anugerah, “A Recommender System for E-Commerce Using Multi-objective Ranked Bandits Algorithm,” in 2018 International Conference on Computing, Engineering, and Design (ICCED), Sep. 2018, pp. 170–174. doi: 10.1109/ICCED.2018.00041.

D. S. Ken Arnett, Z. K. A. Baizal, and Adiwijaya, “Recommender system based on user functional requirements using Euclidean fuzzy,” in 2015 3rd International Conference on Information and Communication Technology (ICoICT), May 2015, pp. 455–460. doi: 10.1109/ICoICT.2015.7231467.

X. Zhao, “A Study on E-commerce Recommender System Based on Big Data,” in 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Apr. 2019, pp. 222–226. doi: 10.1109/ICCCBDA.2019.8725694.

F. Abbas and X. Niu, “Computational Serendipitous Recommender System Frameworks: A Literature Survey,” in 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), Nov. 2019, pp. 1–8. doi: 10.1109/AICCSA47632.2019.9035339.

Z. K. A. Baizal, D. H. Widyantoro, and N. U. Maulidevi, “Computational model for generating interactions in conversational recommender system based on product functional requirements,” Data & Knowledge Engineering, vol. 128, p. 101813, Jul. 2020, doi: 10.1016/j.datak.2020.101813.

B. Patel, P. Desai, and U. Panchal, “Methods of recommender system: A review,” in 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Mar. 2017, pp. 1–4. doi: 10.1109/ICIIECS.2017.8275856.

D. Kluver, M. D. Ekstrand, and J. A. Konstan, “Rating-based collaborative filtering: Algorithms and evaluation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10100 LNCS, Springer Verlag, 2018, pp. 344–390. doi: 10.1007/978-3-319-90092-6_10.

M. I. Ardimansyah, A. F. Huda, and Z. K. A. Baizal, “Preprocessing matrix factorization for solving data sparsity on memory-based collaborative filtering,” in 2017 3rd International Conference on Science in Information Technology (ICSITech), Oct. 2017, pp. 521–525. doi: 10.1109/ICSITech.2017.8257168.

M. R. Zarei and M. R. Moosavi, “A Memory-Based Collaborative Filtering Recommender System Using Social Ties,” in 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), Mar. 2019, pp. 263–267. doi: 10.1109/PRIA.2019.8786023.

Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef, “Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities,” Applied Sciences, vol. 10, no. 21, p. 7748, Nov. 2020, doi: 10.3390/app10217748.

A. Sang and S. K. Vishwakarma, “A ranking based recommender system for cold start & data sparsity problem,” in 2017 Tenth International Conference on Contemporary Computing (IC3), Aug. 2017, pp. 1–3. doi: 10.1109/IC3.2017.8284347.

P. Yu, “Merging Attribute Characteristics in Collaborative Filtering to Alleviate Data Sparsity and Cold Start,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), Mar. 2019, pp. 569–573. doi: 10.1109/ITNEC.2019.8729461.

M. A. Hassan, M. G. M. Johar, and A. I. Hajamydeen, “A Framework for Recommender Systems Using Improved Collaborative Filtering,” in 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), Jun. 2019, pp. 168–173. doi: 10.1109/I2CACIS.2019.8825046.

T. Anwar, T. Siswantining, D. Sarwinda, S. M. Soemartojo, and A. Bustamam, “A study on missing values imputation using K-Harmonic means algorithm: Mixed datasets,” in AIP Conference Proceedings, Dec. 2019, vol. 2202. doi: 10.1063/1.5141651.

R. He and J. McAuley, “Ups and Downs,” in Proceedings of the 25th International Conference on World Wide Web, Apr. 2016, pp. 507–517. doi: 10.1145/2872427.2883037.

Y. Liu, Y. Mu, K. Chen, Y. Li, and J. Guo, “Daily Activity Feature Selection in Smart Homes Based on Pearson Correlation Coefficient,” Neural Processing Letters, vol. 51, no. 2, pp. 1771–1787, Apr. 2020, doi: 10.1007/s11063-019-10185-8.

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Article History
Submitted: 2022-01-22
Published: 2022-03-31
Abstract View: 152 times
PDF Download: 159 times
How to Cite
P, K. A. S. A. V., & Baizal, Z. K. A. (2022). Improved Collaborative Filtering Recommender System Based on Missing Values Imputation on E-Commerce. Building of Informatics, Technology and Science (BITS), 3(4), 453-459.