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About This Tutorial
About This Tutorial
Digital finance is an increasingly important component of everyday life. Digital banking has become the norm, over 60% of working-age adults in the U.S. hold stock market investments, and the advent of large generative models has disrupted the market, resulting in new financial AI tools as well as increased task automation. However, finance is a specialist domain with unique challenges and considerations, making it a particularly rich space for researchers seeking difficult yet impactful problems to explore.
Financial applications are heavily dependent on personalization to be effective and require deep user understanding. Financial search and QA systems demand a strong grasp of temporal dynamics and must aggregate evidence from multiple sources. Moreover, there is a focus not merely on serving information, but on facilitating user understanding, as financial information retrieval systems support high-impact decision-making.
We believe that digital finance will become a much more prevalent use case in SIGIR and related venues in the coming years. Therefore, we propose this tutorial as a means to introduce and skill-up the IR community in state-of-the-art digital finance research and applications. In particular, this full-day tutorial covers the integration of information retrieval, recommender systems, and large language models in financial applications such as investment recommendation, portfolio selection, and research management. Through interdisciplinary insights and real-world case studies, we aim to provide attendees with a comprehensive understanding of how modern IR and NLP techniques are transforming the financial landscape.
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