Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

Zainur Romadhon; Eko Sediyono; Catur Edi Widodo

doi:10.22219/kinetik.v5i3.1062

Issue

Vol. 5, No. 3, August 2020

Issue Published : Aug 31, 2020

Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

https://doi.org/10.22219/kinetik.v5i3.1062

Zainur Romadhon

Universitas Diponegoro

Eko Sediyono

Universitas Kristen Satya Wacana

Catur Edi Widodo

Universitas Diponegoro

Corresponding Author(s) : Zainur Romadhon

zainur.rmdn@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 5, No. 3, August 2020
Article Published : Jul 31, 2020

Abstract

The Recommendation System plays an increasingly important role in our daily lives. With the increasing amount of information on the internet, the recommendation system can also solve problems caused by increasing information quickly. Collaborative filtering is one method in the recommendation system that makes recommendations by analyzing correlations between users. Collaborative filtering accumulates customer item ratings, identifies customers with common ratings, and offers recommendations based on inter-customer comparisons. This study aims to build a system that can provide recommendations to users who want to order or choose fast food menus. This recommendation system provides recommendations based on item data calculations with customer review data using a collaborative filtering approach. The results of applying cosine similarity calculation to determine fast food menu recommendations obtained for the item-based recommendation is Pizza Frankfurter BBQ Large with a value of 1.0, item-based with genre recommendation is Calblend Float with value 1.0 and user-based recommendation is Pizza Black Pepper Beef / Chicken Large with mean score 2.5.

Keywords

Recommendation System Collaborative Filtering Cosine Similarity

Romadhon, Z., Sediyono, E., & Widodo, C. E. (2020). Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(3), 179-186. https://doi.org/10.22219/kinetik.v5i3.1062

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References

G. Xu, Z. Tang, C. Ma, Y. Liu, and M. Daneshmand, “A collaborative filtering recommendation algorithm based on user confidence and time context,” J. Electr. Comput. Eng., vol. 2019, 2019, doi: 10.1155/2019/7070487.
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X. Yang, Y. Guo, Y. Liu, and H. Steck, “A survey of collaborative filtering based social recommender systems,” Comput. Commun., 2014, doi: 10.1016/j.comcom.2013.06.009.
P. Moradi and S. Ahmadian, “A reliability-based recommendation method to improve trust-aware recommender systems,” Expert Syst. Appl., 2015, doi: 10.1016/j.eswa.2015.05.027.
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X. Ramirez-Garcia and M. García-Valdez, “Post-filtering for a restaurant context-aware recommender system,” Stud. Comput. Intell., 2014, doi: 10.1007/978-3-319-05170-3_49.
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A. Gupta and K. Singh, “Location based personalized restaurant recommendation system for mobile environments,” in Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013, 2013, doi: 10.1109/ICACCI.2013.6637223.
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Z. K. A. Baizal, D. H. Widyantoro, and N. U. Maulidevi, “Factors influencing user’s adoption of conversational recommender system based on product functional requirements,” Telkomnika (Telecommunication Comput. Electron. Control., 2016, doi: 10.12928/TELKOMNIKA.v14i4.4234.
Y. Zheng, L. Li, and F. Zheng, “Context-awareness support for content recommendation in e-learning environments,” in 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII 2009, 2009, doi: 10.1109/ICIII.2009.434.
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J. W. Yodha and A. W. Kurniawan, “Pengenalan Motif Batik Menggunakan Deteksi Tepi Canny Dan K-Nearest Neighbor,” Techno.COM, 2014.
K. Alkhatib, H. Najadat, I. Hmeidi, and M. K. A. Shatnawi, “Stock Price Prediction Using K-Nearest Neighbor Algorithm,” Int. J. Business, Humanit. Technol., 2013.
R. Saptono, H. Prasetyo, and A. Irawan, “Combination of cosine similarity method and conditional probability for plagiarism detection in the thesis documents vector space model,” J. Telecommun. Electron. Comput. Eng., vol. 10, no. 2–4, pp. 139–143, 2018.
F. Mohammadi, “A New Approach To Focused Crawling : Combination of Text summarizing With Neural Networks and Vector Space Model,” vol. 2, no. 3, pp. 31–36, 2013.
J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Syst., 2013, doi: 10.1016/j.knosys.2013.03.012.
J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decis. Support Syst., 2015, doi: 10.1016/j.dss.2015.03.008.

References

G. Xu, Z. Tang, C. Ma, Y. Liu, and M. Daneshmand, “A collaborative filtering recommendation algorithm based on user confidence and time context,” J. Electr. Comput. Eng., vol. 2019, 2019, doi: 10.1155/2019/7070487.

K. Choi and Y. Suh, “A new similarity function for selecting neighbors for each target item in collaborative filtering,” Knowledge-Based Syst., 2013, doi: 10.1016/j.knosys.2012.07.019.

X. Yang, Y. Guo, Y. Liu, and H. Steck, “A survey of collaborative filtering based social recommender systems,” Comput. Commun., 2014, doi: 10.1016/j.comcom.2013.06.009.

P. Moradi and S. Ahmadian, “A reliability-based recommendation method to improve trust-aware recommender systems,” Expert Syst. Appl., 2015, doi: 10.1016/j.eswa.2015.05.027.

G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering. 2005, doi: 10.1109/TKDE.2005.99.

H. R. Zhang, F. Min, Z. H. Zhang, and S. Wang, “Efficient collaborative filtering recommendations with multi-channel feature vectors,” Int. J. Mach. Learn. Cybern., 2019, doi: 10.1007/s13042-018-0795-8.

J. Feng, X. Fengs, N. Zhang, and J. Peng, “An improved collaborative filtering method based on similarity,” PLoS One, vol. 13, no. 9, pp. 1–18, 2018, doi: 10.1371/journal.pone.0204003.

X. Su and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques,” Adv. Artif. Intell., 2009, doi: 10.1155/2009/421425.

A. A. Fakhri, Z. K. A. Baizal, and E. B. Setiawan, “Restaurant Recommender System Using User-Based Collaborative Filtering Approach: A Case Study at Bandung Raya Region,” in Journal of Physics: Conference Series, 2019, doi: 10.1088/1742-6596/1192/1/012023.

X. Ramirez-Garcia and M. García-Valdez, “Post-filtering for a restaurant context-aware recommender system,” Stud. Comput. Intell., 2014, doi: 10.1007/978-3-319-05170-3_49.

J. Zeng, F. Li, H. Liu, J. Wen, and S. Hirokawa, “A restaurant recommender system based on user preference and location in mobile environment,” in Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, 2016, doi: 10.1109/IIAI-AAI.2016.126.

A. Gupta and K. Singh, “Location based personalized restaurant recommendation system for mobile environments,” in Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013, 2013, doi: 10.1109/ICACCI.2013.6637223.

N. Jonnalagedda, S. Gauch, K. Labille, and S. Alfarhood, “Incorporating popularity in a personalized news recommender system,” PeerJ Comput. Sci., 2016, doi: 10.7717/peerj-cs.63.

Z. K. A. Baizal, D. H. Widyantoro, and N. U. Maulidevi, “Factors influencing user’s adoption of conversational recommender system based on product functional requirements,” Telkomnika (Telecommunication Comput. Electron. Control., 2016, doi: 10.12928/TELKOMNIKA.v14i4.4234.

Y. Zheng, L. Li, and F. Zheng, “Context-awareness support for content recommendation in e-learning environments,” in 2009 International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII 2009, 2009, doi: 10.1109/ICIII.2009.434.

A. S. Dharma and T. Samosir, “The User Personalization with KNN for Recommender System,” vol. 3, no. 2, pp. 45–48, 2019.

J. S. Breese, D. Heckerman, and C. Kadie, “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” pp. 43–52, 2013, [Online]. Available: http://arxiv.org/abs/1301.7363.

G. A. Pradnyana, I. K. A. Suryantara, and I. G. M. Darmawiguna, “Impression Classification of Endek (Balinese Fabric) Image Using K-Nearest Neighbors Method,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, 2018, doi: 10.22219/kinetik.v3i3.611.

J. W. Yodha and A. W. Kurniawan, “Pengenalan Motif Batik Menggunakan Deteksi Tepi Canny Dan K-Nearest Neighbor,” Techno.COM, 2014.

K. Alkhatib, H. Najadat, I. Hmeidi, and M. K. A. Shatnawi, “Stock Price Prediction Using K-Nearest Neighbor Algorithm,” Int. J. Business, Humanit. Technol., 2013.

R. Saptono, H. Prasetyo, and A. Irawan, “Combination of cosine similarity method and conditional probability for plagiarism detection in the thesis documents vector space model,” J. Telecommun. Electron. Comput. Eng., vol. 10, no. 2–4, pp. 139–143, 2018.

F. Mohammadi, “A New Approach To Focused Crawling : Combination of Text summarizing With Neural Networks and Vector Space Model,” vol. 2, no. 3, pp. 31–36, 2013.

J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, “Recommender systems survey,” Knowledge-Based Syst., 2013, doi: 10.1016/j.knosys.2013.03.012.

J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender system application developments: A survey,” Decis. Support Syst., 2015, doi: 10.1016/j.dss.2015.03.008.

Issue

Vol. 5, No. 3, August 2020

Various Implementation of Collaborative Filtering-Based Approach on Recommendation Systems using Similarity

Corresponding Author(s) : Zainur Romadhon

Abstract

Keywords

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References

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