Personalized information retrieval in digital ecosystems

Author

Zhu, Dengya ; Dreher, Heinz

Author_Institution

Curtin Univ. of Technol., Perth, WA

fYear

2008

fDate

26-29 Feb. 2008

Firstpage

580

Lastpage

585

Abstract

Search results personalization is considered a promising approach to boost the quality of text retrieval. In this paper, a personalized information retrieval paradigm is proposed which not only implicitly creates user profile by learning userspsila search history, search preferences, and desktop information by kNN algorithm; but also intends to deal with the problem of search concepts drift through adjusting the weight of category which represents userspsila search preference. By comparing the cosine similarities between vectors represent personal valued search concepts in user profiles, and vectors represent search concepts in the retrieved search results, the search results will be tailed to better match userspsila information needs.

Keywords

information retrieval; learning (artificial intelligence); pattern classification; user modelling; digital ecosystems; kNN algorithm; machine learning; personalized information retrieval; text retrieval; user profile; users search history; Animals; Australia; Costs; Ecosystems; History; Information retrieval; Machine learning; Natural languages; Search engines; Web search; information retrieval; kNN; machine learning; personalization; user profile;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Ecosystems and Technologies, 2008. DEST 2008. 2nd IEEE International Conference on

Conference_Location

Phitsanulok

Print_ISBN

978-1-4244-1489-5

Electronic_ISBN

978-1-4244-1490-1

Type

conf

DOI

10.1109/DEST.2008.4635207

Filename

4635207