Abstract
This research paper introduces a personalized recommender system tailored specifically for the stock market. With the increasing complexity and variety of investment options, individual investors face significant challenges in making informed decisions. Traditional stock market recommendations often offer generic advice that fails to account for investors’ unique preferences and risk appetites. We propose a personalized recommender system that utilizes deep learning techniques to provide customized stock recommendations. Our approach combines collaborative filtering (CF) and content-based (CB) filtering methodologies which form a hybrid system capable of generating personalized recommendations. Collaborative filtering utilizes the behaviour of similar investors to identify stocks that are likely to appeal to the user, while content-based filtering matches stock characteristics with the user’s preferences and investment history. Experimental evaluations demonstrate that the proposed personalized recommender system outperforms existing algorithms and approaches trained on user interaction data taken from the stock market domain, providing investors with tailored stock recommendations that align with their personal needs and preferences.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
For the source code, visit the Git repository: https://github.com/Stock-Recommender-System-FYP/stock-recommender-system.
References
Kwon, J., Kim, S.: Friend recommendation method using physical and social context. Int. J. Comput. Sci. Netw. Secur. 10(11), 116–120 (2010)
Sharaf, M., Hemdan, E.E.-D., El-Sayed, A., El-Bahnasawy, N.A.: A survey on recommendation systems for financial services. Multimed. Tools Appl. 81(12), 16761–16781 (2022). https://doi.org/10.1007/s11042-022-12564-1
Stock exchanges around the world, Investopedia. https://www.investopedia.com/financial-edge/1212/stock-exchanges-around-the-world.aspx. Accessed 30 May 2023
Chikwira, C., Mohammed, J.I.: The impact of the stock market on liquidity and economic growth: evidence of volatile market. In: Proceedings of the 25th International Conference on Economics, vol. 11, no. 6, pp. 155 (2023). https://doi.org/10.3390/economies11060155
Nair, B.B., Mohandas, V.P.: An intelligent recommender system for stock trading. Intell. Decis. Technol. 9(3), 243–269 (2015). https://doi.org/10.3233/IDT-140220
Gottschlich, J., Hinz, O.: A decision support system for stock investment recommendations using collective wisdom. Decis. Support Syst. 59(1), 52–62 (2014). https://doi.org/10.1016/j.dss.2013.10.005
Paranjape-Voditel, P., Deshpande, U.: A stock market portfolio recommender system based on association rule mining. Appl. Soft Comput. J. 13(2), 1055–1063 (2013). https://doi.org/10.1016/j.asoc.2012.09.012
Bobadilla, J., Alonso, S., Hernando, A.: Deep learning architecture for collaborative filtering recommender systems. Appl. Sci. 10(7), 2441 (2020). https://doi.org/10.3390/app10072441
Batmaz, Z., Yurekli, A., Bilge, A., Kaleli, C.: A review on deep learning for recommender systems: challenges and remedies. Artif. Intell. Rev. 52(1), 1–37 (2019). https://doi.org/10.3390/app122111256
Taghavi, M., Bakhtiyari, K., Scavino, E.: Agent-based computational investing recommender system. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 455–458. Association for Computing Machinery, Hong Kong (2013). https://doi.org/10.1145/2507157.2508072
Shen, Y., Liu, T., Liu, W., Xu, R., Li, Z., Wang, J.: Deep reinforcement learning for stock recommendation. J. Phys: Conf. Ser. 2050(1), 12012 (2021). https://doi.org/10.1088/1742-6596/2050/1/012012
Broman, N.: Comparison of recommender systems for stock inspiration (2021)
Swezey, R.M.E., Charron, B.: Large-scale recommendation for portfolio optimization. In: Proceedings of the 12th ACM Conference on Recommender Systems, pp. 382–386. Association for Computing Machinery, Canada (2018). https://doi.org/10.1145/3240323.3240386
Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 World Wide Web Conference, pp. 689–698. International World Wide Web Conferences Steering Committee, Lyon (2018). https://doi.org/10.1145/3178876.3186150
Truong, Q.-T., Salah, A., Lauw, H.W.: Bilateral variational autoencoder for collaborative filtering. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 292–300 (2021). https://doi.org/10.1145/3437963.3441759
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 639–648 (2020). https://doi.org/10.1145/3397271.3401063
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182 (2017). https://doi.org/10.1145/3038912.3052569
Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets, pp. 319–353. Cambridge University Press (2011)
Lops, P., De Gemmis, M., Semeraro, G.: Content-based recommender systems: state of the art and trends. Recomm. Syst. Handb. 73–105 (2011). https://doi.org/10.1007/978-0-387-85820-3_3
Li, X., She, J.: Collaborative variational autoencoder for recommender systems. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 305–314 (2017). https://doi.org/10.1145/3097983.3098077
Chikhaoui, B., Chiazzaro, M., Wang, S.: An improved hybrid recommender system by combining predictions. In: Proceedings of the 2011 IEEE Workshops of International Conference on Advanced Information Networking and Applications, pp. 644–649 (2011). https://doi.org/10.1109/WAINA.2011.12
Oyebode, O., Orji, R.: A hybrid recommender system for product sales in a banking environment. J. Bank. Financ. Technol. 4, 15–25 (2020). https://doi.org/10.1007/s42786-019-00014-w
CSE Homepage. https://www.cse.lk/. Accessed 30 May 2023
He, X., Chen, T., Kan, M.-Y., Chen, X.: TriRank: review-aware explainable recommendation by modeling aspects. In: CIKM, pp. 1661–1670 (2015). https://doi.org/10.1145/2806416.2806504
Jurafsky, D., Martin, J.H.: Speech and Language Processing. Pearson (2019)
Acknowledgements
This research is conducted by the Department of Computer Science and Engineering of the University of Moratuwa. We gratefully acknowledge the support from IronOne Technologies with their provision of the datasets, domain knowledge and computational resources in this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
Disclaimer
It is advisable to interpret the results of experiments involving stock market transactions with caution due to the personalized behaviour considered.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wijerathne, N. et al. (2024). Deep Learning Based Personalized Stock Recommender System. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1966. Springer, Singapore. https://doi.org/10.1007/978-981-99-8148-9_29
Download citation
DOI: https://doi.org/10.1007/978-981-99-8148-9_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8147-2
Online ISBN: 978-981-99-8148-9
eBook Packages: Computer ScienceComputer Science (R0)