Pattern mining for a two-stage information filtering system
Paper
Paper/Presentation Title | Pattern mining for a two-stage information filtering system |
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Presentation Type | Paper |
Authors | Zhou, Xujuan (Author), Li, Yuefeng (Author), Bruza, Peter (Author), Xu, Yue (Author) and Lau, Raymond Y. K. (Author) |
Editors | Huang , J.Z., Cao, L. and Srivastava , J. |
Journal or Proceedings Title | Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining |
Journal Citation | 6634 (1), pp. 363-374 |
Number of Pages | 12 |
Year | 2011 |
Publisher | Springer |
Place of Publication | Germany |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-642-20841-6_30 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-642-20841-6_30 |
Conference/Event | 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011) |
Event Details | 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2011) Parent Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Delivery In person Event Date 24 to end of 27 May 2011 Event Location Shenzhen, China |
Abstract | As information available over computer networks is growing exponentially, searching for useful information becomes increasingly more difficult. Accordingly, developing an effective information filtering mechanism is becoming very important to alleviate the problem of information overload. Information filtering systems often employ user profiles to represent users’ information needs so as to determine the relevance of documents from an incoming data stream. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed two-stage filtering model significantly outperforms both the term-based and pattern-based information filtering models. |
Keywords | pattern mining, information filtering, user profile, threshold |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460999. Information systems not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science (Book series) |
Byline Affiliations | Queensland University of Technology |
City University of Hong Kong, China | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q3y88/pattern-mining-for-a-two-stage-information-filtering-system
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