Because of the flourishing growth of Internet and IT there are too much information surround us today. We have limited attention but unlimited information. So almost all people today face a novel problem¡X Information Overload. It means our precious resource¡X attention, which is not enough to be used to digest any information that we touch. This problem also exists in Literature Digital Libraries.
In today, any Literature Digital Library may collect over one million literatures and documents. Hence a well searching or recommendation mechanism is needful for users. But the traditional ones are not good enough for users. Their searching results may need users to spend more effort to select for users¡¦ true requirement.
So this study tries to propose a new personal document recommendation mechanism to solve this problem. This mechanism use keyword-based association rule mining method to find association rules between documents. Then according to these rules and user¡¦s history preference, the mechanism recommend documents for user that they really want.
After some evaluations, we prove this study¡¦s mechanism actually solve partial information overload problem. And it has good performance on both ¡§Precision¡¨ and ¡§Recall¡¨ indices.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0829103-032522 |
| Date | 29 August 2003 |
| Creators | Tseng, Chien-Ming |
| Contributors | San-Yi Huang, Ping-Yi Chao, FEN-HUI LIN |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0829103-032522 |
| Rights | campus_withheld, Copyright information available at source archive |
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