Implementasi Algoritma New Heuristic Similarity Model (NHSM) Pada Web Based Recommender System

Abstract

Dalam website e-commerce banyak produk atau jasa yang ditawarkan kepada user dan cukup membuat user kebingungan untuk memilih produk atau jasa apa yang akan mereka gunakan. Tetapi seiring berkembangnya pengetahuan dan teknologi, maka ditemukan suatu cara untuk membantu user mempersempit information overloads ini, yaitu dengan menggunakan recommender system. Tujuan penelitian adalah mengimplementasikan algoritma New Heuristic Similarity Model (NHSM) pada web based recommender system berbasis memory based collaborative filtering dan mengukur keakuratan prediksi menggunakan Mean Absolute Error. Metode pengujian menggunakan empat jenis skenario yaitu skenario perhitungan prediction score, perhitungan similarity, pengujian sparse dataset dan dense dataset. Keempat skenario tersebut diuji dengan menggunakan tiga dataset yaitu MovieLens, Jester Joke dan Yahoo Movie. Hasil penelitian menunjukkan bahwa algoritma NHSM dapat diterapkan pada web based recommender system dan keakuratan prediksi semakin baik jika dataset terisi rating penuh (dense dataset) serta hasil similarity mendekati satu. Kata Kunci: Recommender System, New Heuristic Similarity Model (NHSM), Memory Based Collaborative Filtering, Mean Absolute Error. There are many products or services offered to users in the e-commerce website. Those create users\u27 confusion to choose what products or services they will use. Along with science and technology development, then found a way to help users to narrow down the information overloads by using a recommender system. The research objectives are to implement New Heuristic Similarity Model (NHSM) algorithm in web-based recommender system on memory-based collaborative filtering and measuring prediction accuracy using Mean Absolute Error. The testing method uses four scenarios: calculation of prediction score, calculation of similarity, sparse datasets testing and dense datasets testing. The fourth scenario was tested by using three datasets which are MovieLens, Jester Joke and Yahoo Movie. The results showed that NHSM algorithm can be applied to a web-based recommender system. Prediction accuracy will be better if datasets are filled with full rating (dense dataset) and its value of similarity approaching 1. Keywords: Recommender System, New Heuristic Similarity Model (NHSM), Memory Based Collaborative Filtering, Mean Absolute Error. DAFTAR PUSTAKA Adomavicius, G., & Tuzhilin, A. (2005). 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 Vol.17, 734-749. Ahn, H. J. (2007). A Hybrid Collaborative Filtering Recommender System Using a New Similarity Measure. Proceedings of the 6th WSEAS International Conference on Applied Computer Science, 494-498. Bhunje, S. (2014, Mei 29). Retrieved Desember 3, 2014, from The Geek: http://theegeek.com/do-you-know-about-collaborative-filtering/ Cacheda, F., Carneiro, V., Fernandez, D., & Formoso, V. (2011). Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High- Performance Recommender Systems. ACM Transactions on the Web Vol.5. Dennis, A., Wixom, B. H., & Tegarden, D. (2010). Systems Analysis and Design with UML. New Jersey: Wiley. Ekstrand, M. D., Riedl, J. T., & Konstan, J. A. (2010). Collaborative Filtering Recommender System. The Essence of Knowledge: Human-Computer Interaction Vol.4, 81-173. Hafid, Z., Maharani, W., & Firdaus A., Y. (2010). Similarity Measure menggunakan Algoritma Weighted Difference Entropy (WDE) berbasis Memory-based Collaborative Filtering. Bandung: Telkom University. Lee, J., Sun, M., & Lebanon, G. (2012). A Comparative Study of Collaborative Filtering Algorithms. arXiv preprint arXiv:1205.3193. Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X. (2014). A New User Similarity Model to Improve the Accuracy of Collaborative Filtering. Knowledge-Based System, 156-166. Melville, P., & Sindhwani, V. (2010). Recommender Systems. Encyclopedia of Machine Learning (pp. 829-838). Springer US. Navidi, W. (2011). Statistics for Engineers and Scientists. New York: McGraw-Hill. Nugroho, D. S. (2010). Analsis dan Implementasi Perbandingan Metode Cosine Similarity dan Correlation Based Similarity Pada Recommender System Berbasis Item-Based Collaborative Filtering. Bandung: Telkom University. Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B. (2011). Recommender Systems Handbook. New York: Springer. Rodriguez, D. (2011). Recommender Systems. In J. Leskovec, A. Rajaraman, & J. D. Ullman, Mining of Massive Datasets. United Kingdom: Cambridge University Press. Sania, R., Maharani, W., & K, A. P. (2010). Analisis Perbandingan Metode Pearson dan Sperman Correlation pada Recommender System. Konferensi Nasional Sistem dan Informatika, 99-105. Shapira, B., & Rokach, L. (2010). Retrieved Desember 24, 2014, from Ben-Gurion University: medlib.tau.ac.il/teldan-2010/bracha.ppt Su, X., & Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques. Hindawi Publishing Corporation: Advance in Artificial Intelligence. Sugiyono. (2010). Metode Penelitian Pendidikan. Bandung: ALFABETA. Willmott, C. J., & Matsuura, K. (2005). Advantages of the Mean Absolute Error (MAE) the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research Vol.30, 79-82