SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
The nature of statistical learning theory
The nature of statistical learning theory
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Web-collaborative filtering: recommending music by crawling the Web
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Hermes: a notification service for digital libraries
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Relevance Feedback using Support Vector Machines
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Personalized Services for Digital Library
ICADL '02 Proceedings of the 5th International Conference on Asian Digital Libraries: Digital Libraries: People, Knowledge, and Technology
A Personalized Collaborative Digital Library Environment
ICADL '02 Proceedings of the 5th International Conference on Asian Digital Libraries: Digital Libraries: People, Knowledge, and Technology
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Hybrid method for personalized search in scientific digital libraries
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
Hybrid method for personalized search in digital libraries
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Custom ordering on digital library information retrieval
WebMedia '09 Proceedings of the XV Brazilian Symposium on Multimedia and the Web
A social tagging based collaborative filtering recommendation algorithm for digital library
ICADL'11 Proceedings of the 13th international conference on Asia-pacific digital libraries: for cultural heritage, knowledge dissemination, and future creation
Improving re-ranking of search results using collaborative filtering
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
| Hi-index | 0.00 |
Users of a digital book library system typically interact with the system to search for books by querying on the meta data describing the books or to search for information in the pages of a book by querying using one or more keywords. In either cases, a large volume of results are returned of which, the results relevant to the user are not often among the top few. Re-ranking of the search results according to the user's interest based on his relevance feedback, has received wide attention in information retrieval. Also, recent work in collaborative filtering and information retrieval has shown that sharing of search experiences among users having similar interests, typically called a community, reduces the effort put in by any given user in retrieving the exact information of interest. In this paper, we propose a collaborative filtering based re-ranking strategy for the search processes in a digital library system. Our approach is to learn a user profile representing user's interests using Machine Learning techniques and to re-rank the search results based on collaborative filtering techniques. In particular, we investigate the use of Support Vector Machines(SVMs) and k-Nearest Neighbour methods (kNN) for the task of classification. We also apply this approach to a large scale online Digital Library System and present the results of our evaluation.