Article Recommendation in Literature Digital Libraries

• Main Authors: Wen-Chiang Hsiung, 熊文江
• Other Authors: S-Y Hwang
• Format: Others
• Language: en_US
• Published: 2002
• Online Access: http://ndltd.ncl.edu.tw/handle/53993957107342447329

碩士 === 國立中山大學 === 資訊管理學系研究所 === 90 === 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.