Transactions on Machine Intelligence 2821-1693 3 2 2020 06 01 Introduction of an Optimal Portfolio Recommendation System Using Quantum Potential 111 120 208930 10.47176/TMI.2020.111 EN A. Nasiri Gheydari Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Islamic Azad University, Zanjan, Iran M. Afzali Department of Information Technology Engineering, Faculty of Electrical and Computer Engineering, Islamic Azad University, Zanjan, Iran, 0000-0003-3233-8084 Journal Article 2020 03 18 In today's world, with the evolution of science and technology and the increasing complexity of human life, the vast and growing volume of information in various fields has made the decision-making process to achieve the desired goal very challenging. To address this challenge, recommender systems have emerged, striving to suggest the desired and useful option from among the possible choices based on the applicant's interests, needs, and past analyses. A recommender system is a system that, based on the analysis of existing data from variables and applicants, recommends and introduces the most appropriate findings to the applicants. In this paper, using data related to the multi-year performance of several stock companies, we aim to introduce a recommender system that can recommend a suitable portfolio to investors. By a suitable portfolio, we mean one that offers the highest profit with the least risk to the investor. The basis of this work is using a method developed as an interdisciplinary activity by economists and physicists applying the laws governing complex systems, known as quantum potential. Here, we present a model using the quantum potential method, where the input is data related to company performance and stock market data, and the output is an optimal portfolio comprising suitable weights of each company's stocks. https://www.tmachineintelligence.ir/article_208930_bc632a719c0d2bdd5d5cd862fb5bbf50.pdf