Peer-Reviewed Conference Papers
2025
Takehiro Takayanagi, Hiroya Takamura, Kiyoshi Izumi, and Chung-Chi Chen, "Can GPT-4 Sway Experts' Investment Decisions?" 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL25)
Paper
Takehiro Takayanagi, Masahiro Suzuki, Kiyoshi Izumi, Javier Sanz-Cruzado, Richard McCreadie, and Iadh Ounis, "FinPersona: An LLM-Driven Conversational Agent for Personalized Financial Advising," 2025 European Conference on Information Retrieval (ECIR25)
2024
Takehiro Takayanagi, Masahiro Suzuki, Ryotaro Kobayashi, Hiroki Sakaji, and Kiyoshi Izumi, "Is ChatGPT the Future of Causal Text Mining? A Comprehensive Evaluation and Analysis," 2024 IEEE International Conference on Big Data (Big Data24).
Paper
2023
Takehiro Takayanagi, and Kiyoshi Izumi, "Harnessing Behavioral Traits to Enhance Financial Stock Recommender Systems: Tackling the User Cold Start Problem," 2023 IEEE International Conference on Big Data (Big Data23).
Paper
Takehiro Takayanagi, Chung-Chi Chen, and Kiyoshi Izumi, "Personalized Dynamic Recommender System for Investors," The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'23). (Acceptance Rate: 25.12%, 154/613).
Paper Resource
Takehiro Takayanagi, Kiyoshi Izumi, Atsuo Kato, Naoyuki Tsunedomi, and Yukina Abe, "Personalized Stock Recommendation with Investors’ Attention and Contextual Information," The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'23).
ACM
2022
Takehiro Takayanagi, Hiroki Sakaji, and Kiyoshi Izumi, "SETN: Stock Embedding Enhanced with Textual and Network Information," 2022 IEEE International Conference on Big Data (Big Data'22).
IEEE
Journal Articles
Takehiro Takayanagi, and Kiyoshi Izumi, “Context-Aware Stock Recommendations with Stock’s Characteristics and Investors' Traits, ” 2023 IEICE TRANSACTIONS on Information and Systems.
IEICE
Takehiro Takayanagi, and Kiyoshi Izumi, ”Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial Applications, ” New Gener. Comput.
Talks
"Empirical and Experimental Approaches to Understanding Investor Decision-Making in Financial Markets," Invited Talk at Glasgow IR Seminar, October 2024, Glasgow, Scotland, United Kingdom.
"AI Technology for Investor Support: Progress and Future Outlook," Invited Talk at MPT Forum, June 2024, Tokyo, Japan.
Workshop Papers
Takehiro Takayanagi, Bruno Charron, and Marco Visentini-Scarzanella, "Frogs into princes: A generative model to understand the success of product descriptions," The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING'24), the Seventh Workshop on e-Commerce and NLP.
Takehiro Takayanagi, "Information Retrieval in Financial Documents," 2022 4th Conference on Automated Knowledge Base Construction (AKBC) Workshop, Knowledge Graphs in Finance and Economics.
Domestic Conference (Non-Refereed)
Takehiro Takayanagi, Masahiro Suzuki, Ryotaro Kobayashi, Hiroki Sakaji, and Kiyoshi Izumi, "Causal Text Mining in the Era of Large Language Models," 2023 18th Symposium of Young Researcher Association for NLP Studies (YANS).
Takehiro Takayanagi, Hiroki Sakaji, and Kiyoshi Izumi, "Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial Applications", 2023 Proceedings of the Annual Conference of JSAI.
Paper
Takehiro Takayanagi, Hiroki Sakaji, and Kiyoshi Izumi, "Personalization of Stock Recommendations Considering Stock Characteristics and Investor Traits "(In Japanese), 2023 29th Annual Meeting of the Association for Natural Language Processing (NLP).
Paper
Takehiro Takayanagi, Hiroki Sakaji, and Kiyoshi Izumi, "Proposal of a Thematic Stock Extraction Method Using Individual Stock Information and Inter-stock Information" (In Japanese), 2022 Proceedings of the Annual Conference of JSAI.
Paper
Takehiro Takayanagi, Hiroki Sakaji, and Kiyoshi Izumi, "Proposal of a Stock Embedding Method Considering Individual Stock Information and Inter-stock Information" (In Japanese), 2022 28th Annual Meeting of the Association for Natural Language Processing (NLP).
Paper