Improving User Sparse Query Interpretation Through Pseudo-Relevance Retrieval Methods
| dc.contributor.advisor | Huang, Jimmy | |
| dc.contributor.author | Pei, Quanli | |
| dc.date.accessioned | 2025-04-10T10:53:29Z | |
| dc.date.available | 2025-04-10T10:53:29Z | |
| dc.date.copyright | 2024-12-02 | |
| dc.date.issued | 2025-04-10 | |
| dc.date.updated | 2025-04-10T10:53:29Z | |
| dc.degree.discipline | Information Systems and Technology | |
| dc.degree.level | Master's | |
| dc.degree.name | MA - Master of Arts | |
| dc.description.abstract | Despite 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. | |
| dc.identifier.uri | https://hdl.handle.net/10315/42841 | |
| dc.language | en | |
| dc.rights | Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests. | |
| dc.subject.keywords | Information retrieval | |
| dc.subject.keywords | Pseudo relevance feedback | |
| dc.subject.keywords | Large language model | |
| dc.title | Improving User Sparse Query Interpretation Through Pseudo-Relevance Retrieval Methods | |
| dc.type | Electronic Thesis or Dissertation |
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