Information Systems and Technology
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Browsing Information Systems and Technology by Author "Huang, Jimmy"
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Item Open Access Enhancing General Language Models for Biomedical Test Retrieval via Diversified Prior Knowledge(2023-12-08) Huang, Yizheng; Huang, JimmyThe thesis introduces the Diversified Prior Knowledge Enhanced General Language Model (DPK-GLM) to improve the efficacy of general language models in biomedical Information Retrieval (IR). General language models often struggle with biomedical data due to its specialized terminology and the need for precise matching. DPK-GLM tackles these challenges by integrating domain-specific knowledge, thereby enhancing the model's ability to understand and process biomedical information. The framework comprises three core components. The first, Knowledge-based Query Expansion, leverages authoritative biomedical databases to enrich search queries with domain-specific entities. The second, Aspect-based Filter, identifies documents that are highly relevant to the query. The third, Diversity-based Score Reweighting, re-ranks these filtered documents by combining similarity and diversity scores, yielding more accurate results. Experimental tests on public biomedical IR datasets confirm that DPK-GLM significantly improves retrieval performance.Item Open Access Improving User Sparse Query Interpretation Through Pseudo-Relevance Retrieval Methods(2025-04-10) Pei, Quanli; Huang, JimmyDespite the rapid development of information retrieval technology, understanding sparse user query remains a significant challenge. Users often input short, ambiguous, or context-lacking queries when searching, making it difficult for retrieval systems to capture user intent. This thesis focuses on this critical issue and proposes three innovative models based on Pseudo-Relevance Feedback: CNRoc, CLRoc, and LLM-PRF, with the aim of enhancing the performance of retrieval systems. The CNRoc model enriches query expansions by incorporating external conceptual knowledge, enabling it to capture the subtle meanings of query terms and generate more semantically relevant expansion terms. The CLRoc model combines weak and strong relevance signals, utilizing Contrastive Learning to optimize document selection and enhance the alignment between user intent and result documents. The LLM-PRF model integrates Large Language Model to improve the query representation capability of dense retrieval systems, further enhancing the understanding of user intent. Experimental results demonstrate that these models significantly outperform traditional methods in multiple evaluation metrics, providing effective solutions for handling sparse query. Ultimately, this thesis lays the groundwork for future advancements in Information Retrieval, ensuring that users can more effectively retrieve the information they want and make informed decisions.Item Open Access Stock Price Prediction Using Sentiment and Technical Analysis(2025-04-10) Gao, Huaqi; Huang, JimmyWith the rapid advancement of the economy, the stock market has garnered extensive attention in both business and academic fields. Due to the dynamic, unstable, information-sensitive nature of the stock market, obtaining an accurate stock price prediction is extremely challenging. This study explores the integration of sentiment data from financial news headlines with historical stock data to predict stock prices. This research gathered historical price and trading volume data for the S&P 500 index, sourced from Yahoo Finance, along with 106,494 financial news titles obtained from Reuter. This dataset encompasses the period around the 2008 financial crisis from Oct 20, 2006, to Nov 19, 2013. Empirical implementation of the proposed methodology revealed the substantial value of incorporating sentiment and historical information to enhance the accuracy of stock price prediction.Item Open Access Unveiling the Complexities of Student Satisfaction in E-learning: An Integrated Framework for the Context of COVID-19(2024-03-16) Lin, Rui; Huang, JimmyAmidst the global pandemic’s reshaping of education, our study investigates e-learning dynamics in Canadian higher education. Integrating the Technology Acceptance Model (TAM), the DeLone and McLean Information Systems Success Model (D&M ISS), and the Expectation Confirmation Model (ECM), we introduce the innovative C-RES framework. This framework, which stands for COVID-19 Remote E-learning System, uniquely addresses the complexities of e-learning systems and their role in student satisfaction during COVID-19. Through Structural Equation Modeling (SEM) analysis of responses from a diverse pool of graduate students across Canada, we uncover relationships among psycho- logical factors, quality dimensions, and social influences. We demonstrate how self-efficacy, IT anxiety, and perceived system and information quality significantly influence students’ ease of use and usefulness perceptions, impacting their satisfaction and commitment to Learning Management Systems (LMS). Our findings reveal that e-learning quality lies not only in technology but also in content, and highlight the significant influence of individual confidence and community dynamics on student experiences. These insights provide actionable strategies for enhancing the effectiveness and resilience of e-learning systems, especially in crises. While focusing on the Canadian pandemic context, our research suggests exploring demographic influences in future studies. This thesis serves as a foundation for future e-learning explorations, pushing educational technology boundaries during global disruptions and offering key strategies for resilience and effectiveness in higher education.