Cryptocurrency Recommendation System Based on Investor Preferences Using Knowledge Graph Convolutional Network

Abstract

Cryptocurrency is a digital asset created using blockchain technology and it has different properties from money that we have known such as US Dollar, Rupiah, Yen, etc. Right now, people use cryptocurrency as an investment instrument. However, choosing a cryptocurrency as an investment instrument is challenging because investors need to consider many attributes of a cryptocurrency project. Also, the number of cryptocurrencies is growing each day. Currently, there are at least 20,000 cryptocurrencies to choose from. Developing a recommender system can shorten investors' cryptocurrency screening process. The recommendation system uses Knowledge Graph Convolutional Network (KGCN). It is a model that effectively reveals the relationship between items by mining the attributes of the items in the knowledge graph. Thus, KGCN is suitable for creating a personalized recommendation. The cryptocurrency knowledge graph used in the study is based on the cryptocurrency dataset on the CoinGecko website crawled using its API. The output from the system is in the form of the top 3 cryptocurrency recommendations for specific users and AUC evaluation scores. The research results show that KGCN can provide relevant recommendation results based on investor's preferences up to 17.55% better evaluation scores compared to other models such as RippleNet.