文獻數位圖書館將重要的文獻數位化後永久保存，並且提供研究者查詢其相關領域的研究。而藉由網際網路的幫助，文獻數位圖書館將數位化後的各種文獻置於網際網路上，使的研究者可以位於世界各地透過瀏覽器查詢。然而在查詢相關文獻的時候，往往得到非常龐大的文獻列表，之中對研究者真正有興趣的可能只有少部份，為了要提供更有效率的查詢服務，很多文獻數位圖書館也提供了推薦系統，推薦系統根據過去使用統計和使用者目前的查詢紀錄而預測使用者可能對哪些文章有興趣。本研究將目前應用於網頁的推薦技術將之應用於文獻數位圖書館之文獻推薦。對於文獻數位圖書館的推薦，提出一個架構，此架構包含三個循序的步驟: web log處理、資料採礦、與文章推薦。其中web log處理方面提出三種從web log中擷取出transaction的定義方式，資料採礦應用MSapriori，推薦方式則應用了hypergraph與association based 推薦技術。最後我們用中山大學學位論文系統的web log來評估推薦方法的precision、recall、與執行時間。

Literature digital libraries is perhaps one of the most important resources to research as the preserved literature data is vital to any researchers and practitioners who need to now what people have done previously in a particular area. The emergence of World Wide Web (www) further boosts the circulation power of literature digital libraries, and people who are interested in a particular topic may easily find related articles by searching a literature digital library that provides a www interface. However, it is quite often that a given search condition will 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 literature digital libraries are equipped with a recommendation subsystem that recommend articles to a user based on his past or current interest. In this thesis, we adapt the existing approaches for web page recommendation to the recommendation of literature digital libraries. We have investigated issues for article recommendation of a literature digital library. We have developed a recommendation framework in this context that makes use of web log of a literature digital library. This framework consists of three sequential steps: data preparation of the web log, association discovery, and article recommendations. We proposed three alternatives in identifying transactions from a web log, adapted the MSApriori algorithm for discovery large itemsets, and discussed two approaches, namely hypergraph and association based recommendations, for making recommendation. These alternatives and approaches were evaluated using the web log of an operational electronic thesis system at NSYSU. It has been found that query-chosen and session-chosen are better methods for transaction identification, and hypergraph based approach yields better quality of article recommendation and has stable running time.

Chapter 1 Introduction 1
Chapter 2 Literature Review 4
2.1 General recommender systems 4
2.2 Web page recommender systems 6
2.3 Data preparation for web log 9
2.4 Multilevel hypergraph partition 13
2.4.1 Coarsening phase 16
2.4.2 Initial partitioning phase 18
2.4.3 Uncoarseing and refinement phase 18
2.5 Multiple item support Apriori 19
Chapter 3 The Approaches 22
3.1 Preparing article browsing log 23
3.2 Mining article browsing log 26
3.3 Making recommendation 27
3.3.1 Hypergraph-based approach 28
3.3.2 Association rule based approach 29
Chapter 4 Evaluation 31
4.1 Specifying minimum support 32
4.2 Performance metrics 34
4.3 Preliminary experiments for hypergraph-based recommendation 35
4.4 Comparing three transaction identification methods 36
4.5 Comparing hypergraph- and association-based recommendation approaches 39
Chapter 5 Conclusion 47

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