Open AccessProduct recommender system using neural collaborative filtering for marketplace in indonesia
Author(s) -
Arief Faizin,
Isti Surjandari
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/909/1/012072
Subject(s) - recommender system , collaborative filtering , product (mathematics) , computer science , world wide web , business , mathematics , geometry
Marketplace has the potential growth in Indonesia indicated by the continued increase in the number of customers. However, the marketplace has some limitations to deliver personalized purchasing experience. Recommender system can support marketplace to overcome that limitations so that customer can find items or services based on their preferences. This study propose to develop product recommender system based on Neural Collaborative Filtering (NCF) algorithm. The product recommender system to be built is using implicit feedback data in the form of customer purchase data. Implicit feedback is reliable data for building recommendation system. The results have shown that NCF achieve the best performance and outperforms over the other collaborative filtering methods.