Asemi, Asefeh and Asemi, Adeleh and Ko, Andrea (2024) A model for investment type recommender system based on the potential investors based on investors and experts feedback using ANFIS and MNN. Journal of Big Data, 11 (1). ISSN 2196-1115, DOI https://doi.org/10.1186/s40537-024-00965-y.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Adaptive neuro-fuzzy inference system (ANFIS); Investment recommender system; Multimodal neural network; Clustering; JMP; MATLAB; Python; Fuzzy rules; Investor feedback; Expert feedback |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Software Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 22 Oct 2025 02:00 |
| Last Modified: | 22 Oct 2025 02:00 |
| URI: | http://eprints.um.edu.my/id/eprint/46486 |
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