許多文獻資料庫系統使用內容導向技術(content-based)擷取文章給使用者，內容導向技術是根據使用者提供的關鍵字來搜尋文章。另一方面，許多的推薦系統技術根據使用者的長期瀏覽或交易歷史記錄來推薦符合使用者長期的愛好，然而在文獻資料庫系統，通常只擁有使用者短期的愛好並且感興趣的文章通常數量不多。在過去研究已經使用，例如：文章內容、使用者記錄檔與合著網路(coauthorship network)，推薦文章給使用者以滿足短期的愛好。
在本研究，我們整合學者之間所合作文章的主題資訊至合作網路來擴展整個共同作者網路。更具體地說明，我們提出以潛藏狄利克里分配為基礎的合著網路 (LDA-coauthorship-network-based)，此技術使用潛藏狄利克里分配(latent Dirichlet allocation, LDA)與任務導向(task-focused)技術做為文獻的推薦技術。實驗結果顯示我們的方法比傳統的合著網路在所有的實驗環境都更有效，與內容導向技術相比，當每個任務檔(task profile)包含的內容相似度非常相近時，我們的方法比內容導向技術好，但任務檔的內容相似度低時，我們的方法結果較差。因此我們進一步發展一套混合方法，可自動切換內容導向與潛藏狄利克里分配為基礎的合著網路。此方法可根據任務檔中內容相似程度來進行切換至最適合的方法。實驗結果顯示了混合方法在所有實驗環境都表現最優。

Most literature database systems use content-based technique to retrieve articles to users. However, the content-based technique relies on exact keywords provided by users to search for articles the users are interested in. On the other hand, most recommender system techniques are based on user’s long-term browsing/transaction history so as to recommend items that meet users’ long term interest. However, in literature database system, users’ information need is often short-term. Previous works in recommending articles to satisfy users’ short-term interest have utilized article content, usage log, and coauthorship network.
In this study, we extend coauthorship network method and incorporate scholars’ collaboration topics into the coauthorship network. Specifically, we propose a LDA-coauthorship-network-based technique that integrates topic information into links of the coauthorship network using latent Dirichlet allocation (LDA), and a task-focused (short-term) technique is proposed for recommending literature articles. Experimental results show that the proposed approach is more effective than the traditional coauthorship network method under all operating regions. When compared to the content-based technique, it has better performance when each task profile contains articles that are similar in their content but is less effective otherwise. We further develop a hybrid method that switches between content-based technique and LDA-coauthorship-network-based technique based on the content coherence of a task profile. Experimental results show that the hybrid method outperforms all the other methods under all operating regions.

論文審定書 i
致謝 ii
Abstract iii
中文摘要 v
CHAPTER 1 – Introduction 1
1.1 Background 1
1.2 Motivation 1
1.3 Thesis Organization 4
CHAPTER 2 – Literature Review 5
2.1 Recommender Systems 5
2.1.1 Content-Based Recommendation 6
2.1.2 Collaborative Recommendation 8
2.2 Social Network Analysis 9
2.3 Topic Model 10
2.3.1 Latent Dirichlet Allocation 11
2.3.2 Author-Topic Model 13
2.4 Social Network-Based Recommendation 13
CHAPTER 3 – The Approach 16
3.1 Architecture 16
3.2 Topic Model Construction 18
3.3 Constructing LDA Coauthorship Network 19
3.3.1 Definition 19
3.3.2 Extending Authors for Each Article 21
CHAPTER 4 – Evaluations 27
4.1 Data Collection 27
4.1.1 Training Data 27
4.1.2 Testing Data 28
4.2 Performance Benchmarks 29
4.2.1 Content-Based 29
4.2.3 LDA-Coauthoship 31
4.3 Evaluation Design 32
4.3.1 Evaluation Scenarios 32
4.3.2 Performance Metric 32
4.4 Preliminary Experiment 33
4.4.1 Selection of Threshold 33
4.4.2 Selection of The number of Topics in LDA 38
4.5 Experiments and Results 42
4.5.1 The Original Methods 42
4.5.2 The Hybrid Methods 45
4.5.3 Increase of Recommended Articles 49
CHAPTER 5 – Conclusions 53
References 55

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