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博碩士論文 etd-0724108-120542 詳細資訊
Title page for etd-0724108-120542
論文名稱
Title
結合社會網路技術與內容進行文獻資料庫的推薦
Combining Social Networks and Content for Recommendation in a Literature Digital Library
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
2007-06-22
繳交日期
Date of Submission
2008-07-24
關鍵字
Keywords
社會網路推薦方法、推薦系統、社會網路、混合方法、數位圖書館
Social network, Recommender system, Literature digital library, Social network-based recommendation approach, Hybrid approach
統計
Statistics
本論文已被瀏覽 5928 次,被下載 1688
The thesis/dissertation has been browsed 5928 times, has been downloaded 1688 times.
中文摘要
生活在資訊超載的時代,原有的資訊搜尋機制已不敷使用。隨著電子商務的蓬勃發展,利用資訊科技來發掘消費者潛在的需求變得越來越重要。所以各式各樣的推薦技術相繼被提出,而且許多技術已經在網路商店中實際運作。在這些推薦技術當中,以內容推薦方法(content-based recommendation approach)和協同過濾推薦方法(collaborative filtering recommendation approach)是最成功且最常被採用的推薦技術。最近,利用推薦項目的社會網路因素來判斷相關性的社會網路推薦方法(social network-based recommendation approach)已被提出,它在一些情況下表現的比內容推薦方法來的好,而且可以避免推薦的文章具有高度內容相似性卻低品質的問題。因此,我們提出三種結合內容推薦方法及社會網路推薦方法來達到更佳的推薦效果—轉換式(Switching)、比率式(Proportional)、融合式(Fusion)。根據我們的實驗結果,雖然這三種混合的方法在不同情境下各有優劣,但整體而言它們的表現都比單一的推薦方法要好。此外,我們產生了一些與我們收集的文章有高度內容相關的虛構文章,藉由這些虛構文章的加入來驗證每個方法的精確度。實驗結果顯示利用社會網路資訊能有效降低推薦虛構文章的機會。
Abstract
Living in an information-overloading age, the original information searching mechanisms are ineffective and impractical. As the e-commerce is more and more popular, using information technology to discover the latent demand of customers becomes an important issue. Hence, a variety of recommendation techniques have been proposed and many of them have been implemented in real systems, mostly in online stores. Among the techniques, the content-based and collaborative filtering approaches are the ones broadly adopted and proved to be successful. Recently, social network-based recommendation approach has been proposed that takes into account the similarities of items with respect to their social closeness. The social network-based approach performs better than content-based approach in some scenarios and it can also avoid recommending articles that have high content similarity to a user’s favorite articles but low quality. Therefore, we propose three hybrid approaches, Switching, Proportional, and Fusion
that combine content-based and social network-based approaches in order to achieve a better performance. Our experimental result shows that even though the proposed approaches have pros and cons under different scenarios, in general they achieve better performance than individual
approaches. Besides, we generate some synthetic articles that have close content similarities to articles in our collection to evaluate the fidelity of each approach. The experimental results show that approaches incorporating social network information have lower chance to recommend these faked articles.
目次 Table of Contents
CHAPTER 1 - Introduction....................................................... 1
1.1 Background ........................................................................ 1
1.2 Motivation............................................................................ 2
CHAPTER 2 - Literature Review............................................ 4
2.1 Recommender Systems................................................. 4
2.1.1 Content-based Recommendation ............................. 5
2.1.3 Collaborative Filtering................................................... 6
2.1.3 Hybrid Approaches........................................................ 8
2.1.4 Task-Focused Approach ............................................. 9
2.2 Social Network Analysis ................................................. 9
2.2.1 The Elements of Social Network .............................. 10
2.2.2 The Structural Properties of Social Network........... .11
2.2.3 Graph Theory ................................................................ 11
2.2.4 Ways for Forming Social Networks .......................... 12
2.3 Social Network-Based Recommendation...................14
CHAPTER 3 - Hybrid Approaches ...................................... 20
3.1 Architecture ...................................................................... 20
3.2 Hybrid Approaches ......................................................... 21
CHAPTER 4 - Evaluations ................................................... 24
4.1 Data Collection ................................................................24
4.1.1 Expanded Articles ....................................................... 25
4.1.2 Synthetic Articles ........................................................ 25
4.2 Data Processing ........................................................... 27
4.2.1 Constructing the Content Similarity between Articles...........27
4.2.2 Constructing the Social Similarity between Articles ............... 27
4.3 Experiments and Results ........................................... 28
4.3.1 Experiment Design ................................................... 28
4.3.2 Experimental Results ............................................... 29
4.3.3 Fidelity .......................................................................... 45
CHAPTER 5 - Conclusion ................................................. 51
References .......................................................................... 52
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