Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Revealing the Retail Black Box by Interaction Sensing
ICDCSW '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
A system for generating user's chronological interest space from web browsing history
International Journal of Knowledge-based and Intelligent Engineering Systems
Efficient sequential access pattern mining for web recommendations
International Journal of Knowledge-based and Intelligent Engineering Systems
Data Mining for Design and Marketing
Data Mining for Design and Marketing
A statistical approach to mechanized encoding and searching of literary information
IBM Journal of Research and Development
String analysis technique for shopping path in a supermarket
Journal of Intelligent Information Systems
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Methods for extracting users' interests from their behaviors in the real world and on the Web have been studied. Radio Frequency Identification RFID is a sensor which has been employed in logistics for tracing the movements of products, and can be extended to a tool for obtaining the behavioral data of humans. If RFID is used in a bookstore, pick-up behaviors and browsing time are to be obtained. In this paper, we hypothesize that the longer a book is picked and read, the stronger the user is interested in the book. We propose a system for extracting keywords representing user's interests from his/her behavioral data, i.e., the history of browsing books stored in bookshelves. The proposed system identifies books that have been browsed by a user, and extracts keywords that appear frequently in the books. Then the system weights each keyword using the browsing time of books including the keyword. The system finally outputs keywords of high weights as the indices of the user's interest. The experimental results support our hypothesis, i.e. the keywords obtained by the proposed system represent users' interests more precisely than previous indexing methods.