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| Title: | 建構引導式對話功能於大語言模型上的房地產資訊系統
Construct a Guided Dialogue Function on Large Language Model for Real Estate Information System |
| Authors: | 何世堯
Ho, Shih-Yao |
| Contributors: | 胡毓忠
Hu, Yuh-Jong
何世堯
Ho, Shih-Yao |
| Keywords: | 引導式對話
大型語言模型
槽位填充
檢索增強生成
AWS Bedrock
Guided dialogue
Large Language Models
Slot filling
Retrieval-Augmented Generation
AWS Bedrock |
| Date: | 2025 |
| Issue Date: | 2025-11-03 14:45:17 (UTC+8) |
| Abstract: | 本研究探討在房地產資訊系統中，結合大型語言模型（Large Language Model, LLM）與引導式對話是否能有效降低使用者的認知負擔，並提升需求澄清與推薦的精準度。研究以 Conversation Routines 為提示設計框架，在AWS Bedrock 建置一套分工式架構：由 Bedrock Agent 依 Conversation Routines 規範與槽位填充機制主導互動流程，完成地區、預算、生活機能與其他條件的蒐集；其後透過 RAG 與外部即時搜尋整合知識，並交由 Foundation Model 統整產生推薦結果與可解釋文字。本研究設計六個真實購屋任務情境（明確 ×3、模糊 ×3），在互動式資訊檢索（Interactive Information Retrieval, IIR）觀點下，與產業代表系統「永慶 AI 特助」進行比較；指標包含結果符合度、互動輪數與需求澄清能力。結果顯示：在明確需求情境下，兩系統皆能於單輪內產出可用結果；在模糊需求情境下，本研究系統能透過多輪追問收斂條件，維持推薦品質，並於「完全模糊」情境中仍提供具體建議與物件，而比較系統則無法回應。雖然本系統在模糊情境下通常需要較多互動輪數，但換得較高的需求釐清與建議品質。綜合而言，本研究驗證了引導式對話於高度動態之房地產場域的可行性與實用性，並提供一套可複用的對話設計準則與系統化實作流程。
This study investigates whether integrating Large Language Models (LLMs) with guided dialogue in real estate information systems can effectively reduce users’cognitive load and improve requirement clarification and recommendation accuracy. The research adopts Conversation Routines as the prompt design framework and builds a modular architecture on AWS Bedrock: the Bedrock Agent, guided by Conversation Routines and a slot-filling mechanism, manages the interaction process to collect region, budget, amenities, and other conditions. Subsequently, knowledge is integrated through Retrieval-Augmented Generation (RAG) with AWS OpenSearch Service and real-time external search via the Perplexity API, with the results consolidated by the Foundation Model (Claude 3.7 Sonnet) into recommendations and explanatory text. Six real-world housing purchase scenarios (three clear, three ambiguous) were designed and compared with an industry benchmark system, “Yung-Ching AI Assistant,”under the perspective of Interactive Information Retrieval (IIR). Evaluation indicators included result relevance, number of dialogue turns, and requirement clarification ability. The results show that in clear-demand scenarios, both systems can generate usable results within a single turn; in ambiguous-demand scenarios, our system refines conditions through multiple prompts, maintaining recommendation quality, and even in “completely ambiguous”cases it can still provide concrete suggestions and listings, whereas the comparison system could not respond. Although our system typically requires more dialogue turns in ambiguous scenarios, it yields superior requirement clarification and recommendation quality. Overall, this study validates the feasibility and practicality of guided dialogue in the dynamic real estate domain and provides a reusable set of dialogue design guidelines and a systematic implementation framework. |
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| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
109971020 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109971020 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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