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博碩士論文 etd-0728110-012135 詳細資訊
Title page for etd-0728110-012135
論文名稱
Title
根據消費者產品評論產生個人化推薦
Personalized Recommendation Based on Consumer Product Reviews
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
63
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2010-07-26
繳交日期
Date of Submission
2010-07-28
關鍵字
Keywords
旅館、線上評論、推薦系統、個人化
hotel, online review, personalization, recommendation system
統計
Statistics
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中文摘要
近年來,愈來愈多的消費者在購買產品前,會透過網際網路搜尋其他消費者發表在網路上相關的產品評論,以協助他們做購買決策。然而,網路上的評論數量太多,導致消費者很難有效從中獲得資訊,因而產生資訊超載的問題。搜尋引擎Yahoo和Google的搜尋功能,的確可以幫助使用者找到某些特定資訊相關的評論,但是其所回傳的評論,仍然超過人類視覺可以處理的容量。
因此,本研究試圖發展出利用消費者產品評論產生個人化推薦的概念以解決上述問題。文中進行實驗來驗證所提方法之有效性,以及比較其與傳統推薦方式在精確率上的優劣,同時說明各推薦方式背後的推薦意涵,最後則是勾勒出新推薦概念的系統雛型。本研究的系統會將搜集來的消費者評論,進一步彙整出有用的資訊,以圖形化的方式呈現,如產品滿意度趨勢圖。讓推薦系統不僅僅對消費者有益,也能為業者帶來好處。
Abstract
Before making a purchase, more and more consumers in recent years are consulting other consumers’ product reviews online, to assist them in making a purchasing decision. However, due to the massive amount of online reviews, consumers can hardly get useful information effectively. Hence, information overload has become a problem. Query functions in search engines like Yahoo and Google can help users find some of the reviews that they need for specific information. Nevertheless, the returned pages from these search engines are still beyond the visual capacity of humans.
Therefore, this study aims to develop a new concept of personalized recommendation based on consumer product reviews to solve the afore-mentioned problem. A series of laboratory experiment examines the effectiveness of the proposed approach and compares this approach with other traditional approaches on precision of recommendation. Meanwhile, the meaning of the recommendation behind each approach is explained. Lastly, the prototype of recommendation system based on the proposed approach is illustrated. Our system can display the trend of the gathered consumer reviews in a graphical way, such as a product satisfaction run chart. The development of recommendation systems is not only beneficial to consumers, but also advantageous to sellers.
目次 Table of Contents
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
1.4 論文架構 3
第二章 文獻探討 4
2.1 電子化口耳相傳(e-WOM) 4
2.2 可信賴的消費者產品評論 5
2.3 推薦技術 6
2.3.1 內容基礎推薦技術 6
2.3.2 協同過濾推薦技術 12
2.3.3 其它推薦技術 13
2.3.4 推薦技術相關研究 14
2.4 利用消費者評論來產生推薦的技術(Review Recommendation) 15
第三章 研究方法 17
3.1 系統架構 17
3.2 系統流程 18
3.3 Review Data Server 18
3.4 Recommendation Process 19
3.5 User Profile Data Server 21
3.6 User Interface 23
3.7 利用評論產生推薦的實際範例 24
3.8 趨勢圖繪製規則 26
第四章 實驗與結果 28
4.1 實驗數據來源 28
4.1.1 消費者評論 29
4.1.2 旅館元資料(Hotel Metadata) 29
4.2 評估準則(Evaluation Criterion) 30
4.3 實驗 32
4.3.1 實驗一 個人化CB 32
4.3.2 實驗二 CF 33
4.3.3 實驗三 利用評論進行個人化推薦 35
4.3.4 實驗四 保留相似使用者的評論進行推薦 39
4.4 綜合比較 40
第五章 系統雛型展示 42
5.1 系統簡介 42
5.2 系統功能 42
5.3 系統外觀 44
第六章 結論與未來研究 50
6.1 結論 50
6.2 未來研究 50
參考文獻 52

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