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All Journal Jurnal Buana Informatika JUTI: Jurnal Ilmiah Teknologi Informasi Telematika Seminar Nasional Informatika (SEMNASIF) Sistemasi: Jurnal Sistem Informasi Matrik : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Progresif: Jurnal Ilmiah Komputer PROGRES PENDIDIKAN Universitas Muhammadiyah Yogyakarta Undergraduate Conference Proceeding Jurnal INFOTEL
Vynska Amalia Permadi
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Journal : Telematika

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Use of Hybrid Methods in Making E-commerce Product Recommendation Systems to Overcome Cold Start Problems Budi Santosa; Muhamad Azam Fuadi; Mangaras Yanu Florestiyanto; Vynska Amalia Permadi; Wilis Kaswidjanti
Telematika Vol 16, No 1: February (2023)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v16i1.2080

Abstract

The large number of users and the items offered in e-commerce make it difficult for buyers to choose the right items and sellers to offer their items to the right buyers. To overcome this problem, a system that can offer and recommend goods automatically, namely a recommendation system is needed. One of the most popular methods used to create a recommendation system is collaborative filtering, the recommendations are created based on similarities in user behavior. Unfortunately, this method has a weakness, namely cold start, where the recommendations will be inaccurate on data that has a lot of new users and items due to minimal historical data regarding user behavior. This problem will be tried to be solved in this study using a hybrid method, where this method combines more than 1 method to create a list of recommendations so that it will cover the shortcomings of each method. This study uses Amazon's e-commerce product and transaction data. The use of the hybrid method in this study can overcome the cold start problem by using switching and mixed methods, by not using the collaborative filtering model on new user recommendations or users who have little interaction. New users will receive recommendations based on the combination of popularity-based and content-based filtering models. This can be seen from the Mean Absolute Error (MAE) value of the model, where the MAE value for the data with a minimum user has at least 3 times rating is 0.566883, for the minimum 7 times, the MAE value is smaller, 0.487553.
Co-Authors Agusdin, Riza Prapascatama Alfriska Deviane Puspita Aliza Hanum Anggani Asep Saepudin Dian Indri Purnamasari Edwina Ayu Christy Frans Richard Kodong Herlina Jayadianti Juwairiah Juwairiah Mangaras Yanu Florestiyanto Muhamad Azam Fuadi Partoyo Partoyo Rezky Putratama Raharjo Santosa, Budi Santoso, Bagus Jati Sylvert Prian Tahalea Tahalea, Sylvert Prian Tohari Ahmad Wilis Kaswidjanti