Product recommender system using neural collaborative filtering for marketplace in indonesia

Arief Faizin, Isti Surjandari

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number012072
JournalIOP Conference Series: Materials Science and Engineering
Volume909
Issue number1
DOIs
Publication statusPublished - 21 Dec 2020
Event2020 International Conference on Advanced Mechanical and Industrial Engineering, ICAMIE 2020 - Cilegon City, Banten, Indonesia
Duration: 8 Jul 20208 Jul 2020

Fingerprint

Dive into the research topics of 'Product recommender system using neural collaborative filtering for marketplace in indonesia'. Together they form a unique fingerprint.

Cite this