An Attentive Self-organizing Neural Model For Text Mining
Abstract
This paper utilizes an attention concept approach in text mining to address the deficiencies of existing keyword search engines. We show how an attention concept in conjunction with a traditional search approach can be used to develop an adaptive text mining model with user-oriented, time-based and attentive knowledge. Without changing a user's search behavior, this paper considers some specific post-search operations as attentive targets for building the personalized interest base. This interest base is further shown on an interest map via the self-organizing map algorithm (SOM). By comparing the personalized interest map, the original search results from a keyword search engine are re-ranked. Experimental results demonstrate that the attentive search mechanism is able to improve user satisfaction. © 2008 Elsevier Ltd. All rights reserved.
Recommended Citation
Hung, C., Chi, Y. L., & Chen, T. Y. (2009). An Attentive Self-organizing Neural Model For Text Mining. Expert Systems with Applications, 36(3 PART 2), pp. 7064-7071. Elsevier.
The definitive version is available at https://doi.org/10.1016/j.eswa.2008.08.037
Department(s)
Business and Information Technology
Keywords and Phrases
Attentive agent; Personalized search; Search engine; Self-organizing map; Web text mining
International Standard Serial Number (ISSN)
0957-4174
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 2009
