NDLI: A pattern mining approach for information filtering systems

Content Provider	Springer Nature Link
Author	Li, Yuefeng Algarni, Abdulmohsen Xu, Yue
Copyright Year	2010
Abstract	It is a big challenge to clearly identify the boundary between positive and negative streams for information filtering systems. Several attempts have used negative feedback to solve this challenge; however, there are two issues for using negative relevance feedback to improve the effectiveness of information filtering. The first one is how to select constructive negative samples in order to reduce the space of negative documents. The second issue is how to decide noisy extracted features that should be updated based on the selected negative samples. This paper proposes a pattern mining based approach to select some offenders from the negative documents, where an offender can be used to reduce the side effects of noisy features. It also classifies extracted features (i.e., terms) into three categories: positive specific terms, general terms, and negative specific terms. In this way, multiple revising strategies can be used to update extracted features. An iterative learning algorithm is also proposed to implement this approach on the RCV1 data collection, and substantial experiments show that the proposed approach achieves encouraging performance and the performance is also consistent for adaptive filtering as well.
Starting Page	237
Ending Page	256
Page Count	20
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	14
Issue Number	3
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-12-14
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Pattern mining Relevance feedback Information filtering Document Preparation and Text Processing Pattern Recognition Data Structures, Cryptology and Information Theory Data Mining and Knowledge Discovery Information Storage and Retrieval
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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