Perbandingan Metode Collaborative Filtering dan Hybrid Semantic Similarity

Imam Fahrurrozi(1*), Estu Muh Dwi Admoko(2), Anang Susilo(3)

(1) Program Studi Komputer dan Sistem Informasi, Departemen Teknik Elektro dan Informatika, Sekolah Vokasi, Universitas Gadjah Mada
(2) Program Studi Komputer dan Sistem Informasi, Departemen Teknik Elektro dan Informatika, Sekolah Vokasi, Universitas Gadjah Mada
(3) Program Studi Komputer dan Sistem Informasi, Departemen Teknik Elektro dan Informatika, Sekolah Vokasi, Universitas Gadjah Mada
(*) Corresponding Author


Recommender system is a component which has been developed for online commerce purposes. In this issue, one of the popular methods that has been widely used is collaborative filtering. However, this method has some drawbacks and needs to be improved. Therefore, in this research a combination of Collaborative Filtering (CF) and semantic similarity method has been compare with original CF, and the result expected reducing some deficiencies on the original collaborative filtering method. Based on the performance tests, the results conclude that the combination can reduce some weaknesses on the original collaborative filtering, especially on the cold-start item and sparsity issue.


Recommender System, Collaborative Filtering, Semantic Similarity, Combination, Cold-start Item, Sparsity Data.

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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, pp. 734-749.


Djamal, R. A., Maharani, W., & Kurniati, A.P. (2010). Analisis dan Implementasi Metode Item-Based Clustering Hybrid pada Recommender System, Konferensi Nasional Sistem dan Informatika, pp. 216-222.


Ganesan, P., Garcia-Molina, H., & Widom, J. (2003) Exploiting Hierarchical Domain Structure to Compute Similarity, ACM Transactions on Information Systems (TOIS), Vol. 21, No.1, pp.64-93.


Guo, X., & Lu, J. (2005). Recommending Trade Exhibitions by Integrating Semantic Information with Collaborative Filtering, Web Intelligence Proceedings IEEE/WIC/ACM International Conference, pp. 747-750.


Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating Collaborative Filtering Recommender Systems, ACM Transactions on Information Systems (TOIS), Vol 22, No.1,pp. 5-53.


Liu, X., Jia, S., Liu, E., & Zhang, Z. (2009). Application of Web-based Data Mining in Personalized Online Recruiting System, International Conference on Management and Service Science, 2009. MASS '09, pp. 1-4.


Montiel, R.M., & Montes, A.J. F. (2009). Semantically Enhanced Recommender Systems., On the Move to Meaningful Internet Systems: OTM 2009 Workshops Springer Berlin Heidelberg, pp. 604-609.


Pedersen T., Pakhomov S. and Patwardhan S. (2007). Measures of Semantic Similarity and Relatedness in the Medical Domain, University of Minnesota Digital Technology Center Research Report DTC 2005/12, Vol. 40, No. 3.


Russell S. and Norvig P. (2003) Artificial intelligence: A Modern Approach 2nd Edition, New Jersey, Prentice Hall.


Saruladha, K. (2011). Semantic Similarity Measures for Information Retrieval Systems Using Ontology, Thesis, Department Computer Science Pondicherry University, India.


Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based Collaborative Filtering Recommendation Algorithms. In Proceedings of the 10th international conference on World Wide Web, pp. 285-295.


Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering Recommender Systems. , In The adaptive web, Springer Berlin Heidelberg. , pp. 291-324.


Shambour, Q. & Lu, J. (2011). A Hybrid Multi-Criteria Semantic-enhanced Collaborative Filtering Approach for Personalized Recommendations, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology , Vol. 1, pp. 71-78.


Sun, X., & Zhao, W. (2009). Design and Implementation of an E-learning Model Based on WUM Techniques, International Conference on E-Learning, E-Business, Enterprise Information Systems, and E-Government, 2009. EEEE '09., pp. 248n-251.


Vozalis, E., & Margaritis, K. G. (2003). Analysis of Recommender Systems Algorithms, Proceedings of the 6th Hellenic European Conference on Computer Mathematics and its Applications , Athens, Greece.


Wanarsup, W., Pattamavorakun, Sn., & Pattamavorakun, St., Intelligent Personalization Job Web Site, Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2008. SNPD '08, pp. 959 – 964.


Zhang, L., Zhang, X., Chen, Q., Zhu, Z., & Shi, Y. (2011). Domain-Knowledge Driven Recommendation Method and Its Application, Computational Sciences and Optimization (CSO) IEEE Fourth International Joint Conference, pp. 21-25.


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