Product Recommendation System Design Using Cosine Similarity and Content-based Filtering Methods

https://doi.org/10.22146/ijitee.45538

Cut Fiarni(1), Herastia Maharani(2*)

(1) Institut Teknologi Harapan Bangsa
(2) Institut Teknologi Harapan Bangsa
(*) Corresponding Author

Abstract


The wide variety of products offered by a company, combined with the consistent demands of specific products from customers, create a certain problem for the organization when they want to market a new product. Organization need information that could help them promote the most suitable product based on their customer’s characteristics. The organization also need to suggest alternative products for customer if the requested product is unavailable. In this research, we design a Recommender System that could suggest either new or alternatif products to customer based on their characteristic and transaction history. This proposed system adopts Cosine Similarity method to calculate product similarity score and Content-based Filtering to calculate customer recommendation score and used as a model for the proposed system. Subsequently, these models are used to classify customers as well as products according to their transaction behavior and consequently recommends new products more likely to be purchased by them. Based on the testing results of the proposed system, it can be concluded that the chosen methods can be utilized to recommend products and costumer of products. It is shown that Precision and Recall of product similarity scores and customer recommendation for product scores are 100% and 93.47%.

Keywords


Product Recommendation; Recommender System; Cosine Similarity; Content-Based Filtering

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References

E.M. Sipayung, C. Fiarni, and R. Tanudjaya, “Decision Support System for Potential Sales Area of Product Marketing Using Classification And Clustering Methods,” Proc. of 8th International Seminar on Industrial Engineering and Management, 2015, pp. DSS.33-39.

A.R. Horrocks and S.C. Anand, Handbook of Technical Textiles, Cambridge, England: Woodhead Publishing Limited in association with the Textile Institute Abington Hall, 2000.

P. Kotler, Manajemen Pemasaran, Milenium Ed., Translation by B. Molan and H. Teguh, Jakarta, Indonesia, 2000.

Kusrini, Konsep dan Aplikasi Sistem Pendukung Keputusan, Yogyakarta, Indonesia: Andi Offset, 2007.

L. Zhiqiang, S. Werimin, and Y. Zhenhua, “Measuring Semantic Similarity between Words Using Wikipedia,” Proc. of 2009 International Conference on Web Information Systems and Mining, 2009, pp. 251-255.

C.C. Aggarwal, Recommender Systems: The Textbook, Cham, Switzerland: Springer, 2016.

H. Maharani and F.A. Gunawan, “Sistem Rekomendasi Mobil Berdasarkan Demographic dan Content-Based Filtering,” Jurnal Telematika ITHB, Vol. 9, No. 2, pp. 64-68, 2014.

P. Lenhart and D. Herzog, “Combining content-based and collaborative filtering for personalized sports news recommendations,” Proc. of the 3rd Workshop on New Trends in Content-Based Recommender Systems, 2016, pp. 3-10.

Z. Lu, Z. Dou, J. Lian, X. Xie and Q. Yang, “Content-Based Collaborative Filtering for News Topic Recommendation,” Proc. of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015, pp. 217-223.

H-P. Kriegel, E. Schubert, and A. Zimek, “The (Black) Art of Runtime Evaluation: Are We Comparing Algorithms or Implementations?” Knowledge and Information Systems, Vol. 52, No. 2, pp. 341–378, 2016.

E.M. Sipayung, H. Maharani, and B.A. Paskhadira, “Designing Customer Target Recommendation System Using K-Means Clustering Method,” International Journal of Information Technology and Electrical Engineering (IJITEE), Vol. 1, No. 1, pp. 1-7, 2017.

D.M. Powers, "Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness, and Correlation," Journal of Machine Learning Technologies, Vol. 2, No. 1, pp. 37-63, 2011.



DOI: https://doi.org/10.22146/ijitee.45538

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