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博碩士論文 etd-0711103-093314 詳細資訊
Title page for etd-0711103-093314
論文名稱
Title
結合內容及合作推薦技術之文獻數位圖書館
Combining Content-based and Collaborative Article Recommendation in Literature Digital Libraries
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
45
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
2003-07-10
繳交日期
Date of Submission
2003-07-11
關鍵字
Keywords
資訊擷取、數位圖書館、推薦系統、個人化服務
Personalization, Information Retrieval, Recommendation System, Literature Digital library
統計
Statistics
本論文已被瀏覽 5901 次,被下載 3848
The thesis/dissertation has been browsed 5901 times, has been downloaded 3848 times.
中文摘要
文獻數位圖書館提供文獻數位化的儲存,研究人員可以透過網路很方便地使用文獻的查詢。然而在查詢文獻的時候,往往一次的查詢會得到相當大量的文獻列表,然而其中真正研究人員有興趣的可能只有極少部分。為了提供更有效率的查詢服務,愈來愈多系統提供推薦服務。系統根據使用統計和使用者的查詢記錄來推薦過去常常一起被瀏覽的文獻,或者是內容相似度高的文獻。文獻推薦系統和傳統推薦系統最主要的不同在於只有瀏覽紀錄而無評比分數,也就是沒有負項的評分作為資訊。

本研究改進合作推薦(Collaborative recommendation)最主要的問題,也就是能夠推薦的文獻佔整體的比率不高。在方法上,首先提出三種利用內容導向過濾(Content-Based Filtering)的方式來推薦,另外提出混和內容導向和合作推薦的合併方式二種。在評估的部分,則是用中山大學學位論文系統的Web log中,找出代表使用者行為的transaction來評估推薦方法的precision、recall,與執行時間。結果發現合併推薦的方式整體而言會有較好的推薦結果。


Abstract
Literature digital libraries are the source of digitalized literature data, from which Researchers can search for articles that meet their personal interest. However, Users often confused by the large number of articles stored in a digital library and a single query will typically yield a large number of articles, among which only a small subset will indeed interest the user. To provide more effective and efficient information search, many systems are equipped with a recommendation subsystem that recommends articles that users might be interested. In this thesis, we aim to research a number of recommendation techniques for making personalized recommendation.

In light of the previous work that used collaborative approach for making recommendation for literature digital libraries, in this thesis, we first propose three content-based recommendation approaches, followed by a set of hybrid approaches that combine both content-based and collaborative methods. These alternatives and approaches were evaluated using the web log of an operational electronic thesis system at NSYSU. It has been found the hybrid approaches yields better quality of articles recommendation.

目次 Table of Contents
CHAPTER 1 INTRODUCTION 1
1.1 RESEARCH BACKGROUND 1
1.2 RESEARCH MOTIVATIONS AND OBJECTIVES 1
CHAPTER 2 LITERATURE REVIEW 4
2.1 INFORMATION RETRIEVAL 4
2.2 RECOMMENDER SYSTEMS 8
2.2.1 Content-based filtering: 8
2.2.2 Collaborative filtering: 9
2.2.3 Web page recommender system 10
2.3 DATA PREPARATION FOR WEB USAGE LOG 13
2.4 MULTIPLE REFERENCE POINT SYSTEMS 15
2.5 MULTILEVEL HYPERGRAPH PARTITION 17
2.5.1 Coarsening phase 20
2.5.2 Initial partitioning phase 21
2.5.3 Uncoarseing and refinement phase 22
CHAPTER 3 CONTENT-BASED AND HYBRID APPROACHES 23
3.1 MULTIPLE REFERENCE POINTS APPROACH 24
3.2 CLUSTERING BASED APPROACHES 25
3.2.1 Feature partitioning 26
3.2.2 Article partitioning approach 27
3.3 HYBRID APPROACHES 28
CHAPTER 4 EVALUATIONS 30
4.1 PERFORMANCE METRICS 32
4.2 EXPERIMENTAL RESULTS 34
CHAPTER 5 CONCLUSIONS 42
REFERENCES 43
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