A model for investment type recommender system based on the potential investors based on investors and experts feedback using ANFIS and MNN

IF 8.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Asefeh Asemi, Adeleh Asemi, Andrea Ko
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Abstract

This article presents an investment recommender system based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and pre-trained weights from a Multimodal Neural Network (MNN). The model is designed to support the investment process for the customers and takes into consideration seven factors to implement the proposed investment system model through the customer or potential investor data set. The system takes input from a web-based questionnaire that collects data on investors' preferences and investment goals. The data is then preprocessed and clustered using ETL tools, JMP, MATLAB, and Python. The ANFIS-based recommender system is designed with three inputs and one output and trained using a hybrid approach over three epochs with 188 data pairs and 18 fuzzy rules. The system's performance is evaluated using metrics such as RMSE, accuracy, precision, recall, and F1-score. The system is also designed to incorporate expert feedback and opinions from investors to customize and improve investment recommendations. The article concludes that the proposed ANFIS-based investment recommender system is effective and accurate in generating investment recommendations that meet investors' preferences and goals.

Graphical abstract

Abstract Image

基于投资者和专家反馈的潜在投资者投资类型推荐系统模型(使用 ANFIS 和 MNN
本文介绍了一种基于自适应神经模糊推理系统(ANFIS)和多模态神经网络(MNN)预训练权重的投资推荐系统。该模型旨在为客户的投资过程提供支持,并考虑了七个因素,通过客户或潜在投资者数据集来实现所建议的投资系统模型。该系统通过网络问卷收集有关投资者偏好和投资目标的数据。然后使用 ETL 工具、JMP、MATLAB 和 Python 对数据进行预处理和聚类。基于 ANFIS 的推荐系统设计了三个输入和一个输出,并使用混合方法对 188 对数据和 18 条模糊规则进行了三次历时训练。该系统的性能使用 RMSE、准确度、精确度、召回率和 F1 分数等指标进行评估。该系统还设计了专家反馈和投资者意见,以定制和改进投资建议。文章的结论是,所提出的基于 ANFIS 的投资推荐系统能有效、准确地生成符合投资者偏好和目标的投资建议。 图表摘要
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来源期刊
Journal of Big Data
Journal of Big Data Computer Science-Information Systems
CiteScore
17.80
自引率
3.70%
发文量
105
审稿时长
13 weeks
期刊介绍: The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
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