Title :
Personalized information retrieval in digital ecosystems
Author :
Zhu, Dengya ; Dreher, Heinz
Author_Institution :
Curtin Univ. of Technol., Perth, WA
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;
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
DOI :
10.1109/DEST.2008.4635207
