CFP last date
16 December 2024
Reseach Article

Beyond Keywords: Understanding User Intent for Personalized Financial Search

by Siddharth Dabhade, Prayas Gajbhiye, Himanshu Thakkar
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 45
Year of Publication: 2024
Authors: Siddharth Dabhade, Prayas Gajbhiye, Himanshu Thakkar
10.5120/ijais2024451984

Siddharth Dabhade, Prayas Gajbhiye, Himanshu Thakkar . Beyond Keywords: Understanding User Intent for Personalized Financial Search. International Journal of Applied Information Systems. 12, 45 ( Oct 2024), 41-47. DOI=10.5120/ijais2024451984

@article{ 10.5120/ijais2024451984,
author = { Siddharth Dabhade, Prayas Gajbhiye, Himanshu Thakkar },
title = { Beyond Keywords: Understanding User Intent for Personalized Financial Search },
journal = { International Journal of Applied Information Systems },
issue_date = { Oct 2024 },
volume = { 12 },
number = { 45 },
month = { Oct },
year = { 2024 },
issn = { 2249-0868 },
pages = { 41-47 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number45/beyond-keywords-understanding-user-intent-for-personalized-financial-search/ },
doi = { 10.5120/ijais2024451984 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-25T00:27:20.366107+05:30
%A Siddharth Dabhade
%A Prayas Gajbhiye
%A Himanshu Thakkar
%T Beyond Keywords: Understanding User Intent for Personalized Financial Search
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 45
%P 41-47
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traditional financial information retrieval systems primarily rely on market data feeds and predefined search options. While these systems offer a diverse overview of financial information, traditional search engines may not always fully understand the specific information needs behind a user's search. This study explores an elegant approach that powers accurate user query analysis to personalize search results and cater to the exact information goals of financial users. The proposed model analyses user queries to understand the intent behind a search. This allows users to get results and recommendations, providing a more nuanced and user-centric experience than traditional approaches. The researchers discuss this approach's potential benefits, including improved user experience, understanding of user intent, and the ability to anticipate user needs. This study also listed the challenges associated with user data collection and privacy concerns. This research contributes to developing comprehensive, user-centric, and personalized next-generation financial information retrieval systems.

References
  1. Odonkor, B., Kaggwa, S., Uwaoma, P. U., Hassan, A. O., & Ajoke Farayola, O. A. (2024). The impact of AI on Accounting Practices: A Review: Exploring how Artificial Intelligence transforms traditional accounting methods and financial reporting. World Journal of Advanced Research and Reviews, 21(1), 172–188. https://doi.org/10.30574/wjarr.2024.21.1.2721
  2. Domain Specific Language Models. Lark. (2023). https://www.larksuite.com/en_us/topics/ai-glossary/domain-specific-language-models
  3. Allan, J., Aslam, J., Belkin, N., Buckley, C., Callan, J., Croft, B., Dumais, S., Fuhr, N., Harman, D., Harper, D. J., Hiemstra, D., Hofmann, T., Hovy, E., Kraaij, W., Lafferty, J., Lavrenko, V., Lewis, D., Liddy, L., Manmatha, R., … Zhai, C. (2003). Challenges in information retrieval and language modeling. ACM SIGIR Forum, 37(1), 31–47. https://doi.org/10.1145/945546.945549
  4. G. Penha, E. Palumbo, M. Aziz, A. Wang, and H. Bouchard, “Improving Content Retrievability in Search with Controllable Query Generation,” in ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, Association for Computing Machinery, Inc, Apr. 2023, pp. 3182–3192. doi: 10.1145/3543507.3583261.
  5. Gidofalvi, G., & Elkan, C. (2001). Using news articles to predict stock price movements. Department of computer science and engineering, university of California, san diego, 17.
  6. Schumaker, R. P., Zhang, Y., Huang, C.-N., & Chen, H. (2012). Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458–464. https://doi.org/10.1016/j.dss.2012.03.001
  7. Wang, D., Su, J., & Yu, H. (2020). Feature extraction and analysis of natural language processing for deep learning English language. IEEE Access, 8, 46335-46345.
  8. Cui, L., Xu, Y., Lv, T., & Wei, F. (2021). Document ai: Benchmarks, models, and applications. arXiv preprint arXiv:2111.08609.
  9. Wu, S., Irsoy, O., Lu, S., Dabravolski, V., Dredze, M., Gehrmann, S., ... & Mann, G. (2023). Bloomberggpt: A large language model for finance. arXiv preprint arXiv:2303.17564.
Index Terms

Computer Science
Information Sciences

Keywords

Domain-Specific Search Engine User Query Analysis Financial Search Information Retrieval Personalization User Intent User-Centric Search Entity Type Filtering and Information Retrieval in Finance