Penggunaan Correlation-Based Similarity untuk Sistem Rekomendasi Tanpa Rating

Authors

  • Hans Yulian Departemen Teknik Informatika, Institut Teknologi Harapan Bangsa
  • Inge Martina Departemen Teknik Informatika, Institut Teknologi Harapan Bangsa

DOI:

https://doi.org/10.61769/telematika.v10i1.121

Keywords:

sistem rekomendasi, rating, correlation-based similarity, treshold

Abstract

Sistem rekomendasi adalah sebuah sistem yang menjadi sebuah kebutuhan banyak perusahaan saat ini, terutama perusahaan yang menjual produk dan melakukan aktivitasnya melalui media web. Rating yang telah diberikan oleh seorang pengguna akan digunakan sebagai referensi untuk menentukan rekomendasi untuk pengguna tersebut serta orang lain yang memiliki karakteristik yang mirip. Terkadang sistem rekomendasi tidak menggunakan rating, tetapi berdasarkan sejarah pembelian/pemakaian. Correlation-based similarity adalah sebuah algoritma yang dapat digunakan untuk mendapatkan nilai kemiripan antar dua obyek yang berbeda. Kemiripan dihitung berdasar rating yang diberikan oleh pengguna. Seorang pengguna dikatakan mirip dengan pengguna lain berdasarkan nilai threshold yang ditentukan. Kadang sulit untuk mendapatkan rating maka diperlukan sistem rekomendasi tanpa rating. Rumus correlation-based similarity perlu dioptimasi untuk sistem rekomendasi tanpa rating.

Recommender system is needed by many companies, mainly for companies that selling products and services in web. User ratings are used as recommendation references for user himself or for another users having similar characteristics. Sometimes recommender system doesn’t use rating, but based on buying/using history. Correlation-based similarity is an algorithm that can be used for getting similarity value between two different objects. Similarity is calculated based on user ratings. Sometimes it is difficult to get rating, so that it is needed a recommender system without user input. Correlation-based similarity formula must be optimized for recommender system without rating.

References

F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Recommender System Handbook, New York, USA: Springer Science+Business Media, 2010.

T. Kim and S. Yang, “An Effective Threshold-based Neighbor Selection”, Advances in Information Retrieval, 2007.

Downloads

Published

2016-12-07

Issue

Vol. 10 No. 1 (2015)

Section

Articles

License

Copyright (c) 2016 Hans Yulian, Inge Martina

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  3. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.