Perbandingan Metode Collaborative Filtering dan Hybrid Semantic Similarity

https://doi.org/10.22146/jntt.44938

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

Abstract


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.


Keywords


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

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DOI: https://doi.org/10.22146/jntt.44938

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