Open AccessThe Hybrid Recommender System of the Indonesian Online Market Products using IMDb weight rating and TF-IDF
Author(s) -
Muhammad Johari,
Arif Dwi Laksito
Publication year - 2021
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v5i5.3486
Subject(s) - recommender system , cosine similarity , computer science , collaborative filtering , crawling , indonesian , social media , similarity (geometry) , world wide web , information retrieval , web crawler , the internet , artificial intelligence , cluster analysis , medicine , linguistics , philosophy , image (mathematics) , anatomy
Today, consumers are faced with an abundance of information on the internet; accordingly, it is hard for them to reach the vital information they need. One of the reasonable solutions in modern society is implementing information filtering. Some researchers implemented a recommender system as filtering to increase customers’ experience in social media and e-commerce. This research focuses on the combination of two methods in the recommender system, that is, demographic and content-based filtering, commonly it is called hybrid filtering. In this research, item products are collected using the data crawling method from the big three e-commerce in Indonesia (Shopee, Tokopedia, and Bukalapak). This experiment has been implemented in the web application using the Flask framework to generate products’ recommended items. This research employs the IMDb weight rating formula to get the best score lists and TF-IDF with Cosine similarity to create the similarity between products to produce related items.