Thu, 07 Aug 2008 21:56:07 BST CiteULike: brusilovsky's digital-library http://www.citeulike.org/user/brusilovsky/tag/digital-library CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/brusilovsky/article/2527086 <i>J. Educ. Resour. Comput., Vol. 6, No. 1. (March 2006)</i> Moving digital libraries into the student learning space: The GetSmart experience Byron Marshall Hsinchun Chen Rao Shen Edward Fox doi:10.1145/1217862.1217864 J. Educ. Resour. Comput., Vol. 6, No. 1. (March 2006) 2008-03-13T14:29:45-00:00 2006 J. Educ. Resour. Comput. 1531-4278 6 1 ACM concept-map digital-library http://www.citeulike.org/user/brusilovsky/article/1133000 <i>Inf. Process. Manage., Vol. 41, No. 1. (January 2005), pp. 5-21.</i> A personalized collaborative digital library environment: a model and an application Elena Renda Umberto Straccia doi:10.1016/j.ipm.2004.04.007 Inf. Process. Manage., Vol. 41, No. 1. (January 2005), pp. 5-21. 2007-03-01T04:13:27-00:00 2005 Inf. Process. Manage. 0306-4573 41 1 5 21 Pergamon Press, Inc. digital-library personalization http://www.citeulike.org/user/brusilovsky/article/1082907 <i>(2004), pp. 465-470.</i> Using Information Visualization for Accessing Learning Object Repositories Joris Klerkx Erik Duval Michael Meire doi:10.1109/IV.2004.140 (2004), pp. 465-470. 2007-02-02T05:46:51-00:00 2004 465 470 IEEE Computer Society digital-library information-retrieval information-visualization http://www.citeulike.org/user/brusilovsky/article/389344 <i>(2001), pp. 331-340.</i> PERSIVAL, a system for personalized search and summarization over multimedia healthcare information Kathleen Mckeown Shih-Fu Chang James Cimino Steven Feiner Carol Friedman Luis Gravano Vasileios Hatzivassiloglou Steven Johnson Desmond Jordan Judith Klavans Andr&\#233; Kushniruk Vimla Patel Simone Teufel doi:10.1145/379437.379722 (2001), pp. 331-340. 2005-11-12T00:33:06-00:00 2001 331 340 ACM Press digital-library personalization http://www.citeulike.org/user/brusilovsky/article/828945 <i>The Journal of Academic Media Librarianship, Vol. 8, No. 1. (2001)</i><br /><br />Libraries serve three roles in the learning process (Marchionini 1995, 66). First, they play a practical role in sharing valuable resources. Although this is a general expectation of users, the digital library revolutionizes this sharing role by allowing people to access materials simultaneously regardless of their physical location. Second, libraries serve a cultural role in preserving and organizing artifacts and ideas. In this sense, libraries serve as reservoirs of information sources for the education of generations. Third, libraries serve a social and intellectual role by bringing people and ideas together. Digital libraries offer diverse information resources extending beyond the physical space shared by groups of learners. In theory, digital libraries bring together people with different learning missions. PERSONALIZATION TOOLS FOR ACTIVE LEARNING IN DIGITAL LIBRARIES Champa Jayawardana Priyantha Hewagamage Masahito Hirakawa The Journal of Academic Media Librarianship, Vol. 8, No. 1. (2001) 2006-09-05T17:11:26-00:00 2001 The Journal of Academic Media Librarianship 8 1 digital-library personalization http://www.citeulike.org/user/brusilovsky/article/162098 <i>(2004), pp. 218-227.</i> Sharing encountered information: digital libraries get a social life Catherine Marshall Sara Bly doi:10.1145/996350.996401 (2004), pp. 218-227. 2005-04-15T19:11:37-00:00 2004 218 227 ACM Press annotation bookmarking digital-library http://www.citeulike.org/user/brusilovsky/article/348191 <i>(1999), pp. 19-25.</i> From reading to retrieval: freeform ink annotations as queries Gene Golovchinsky Morgan Price Bill Schilit doi:10.1145/312624.312637 (1999), pp. 19-25. 2005-10-11T18:01:56-00:00 1999 19 25 ACM Press annotation digital-library information-retrieval http://www.citeulike.org/user/brusilovsky/article/892552 <i>International Journal of Information Management, Vol. 26, No. 3. (June 2006), pp. 234-248.</i><br /><br />Digital libraries (DLs) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from DLs. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in DLs has been user driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct DLs that satisfy a user's necessity for information: Adaptive DLs, libraries that automatically learn user preferences and goals and personalize their interaction using this information. Automated user modeling for personalized digital libraries E Frias-Martinez G Magoulas S Chen R Macredie doi:10.1016/j.ijinfomgt.2006.02.006 International Journal of Information Management, Vol. 26, No. 3. (June 2006), pp. 234-248. 2006-10-11T10:08:30-00:00 2006 International Journal of Information Management 26 3 234 248 digital-library en user-profile http://www.citeulike.org/user/brusilovsky/article/781557 <i>Proceedings of the American Society for Information Science and Technology, Vol. 39, No. 1. (2002), pp. 292-297.</i><br /><br />Qualitatively enhanced access to data collections requires a more meaningful interface which endows the users with personal agents that are easily adaptable to the users' individual retrieval preferences. Tracking individual needs of users leads to personal digital assistance that appears in the system's interaction mode as the user's personal software agent. The main objective of personal assistance as envisaged here is enabling the system to give the users recommendations that are derived from best practive in information search processes. Best practice reflects the knowledge about how users perform successful retrieval activities and attempt to define the best ways of searching large document collections. Best practices are developed on an individual as well as on a community base.The model presented here was developed for the information system IRAIA, a portal for economic information from huge data collections of Economic Research Institutes (ERI) and National Statistical Institutions (NSI). The personal agent draws on IRAIA's model of context-oriented retrieval and observe, record and analye the users' search and navigation by applying concepts of the respective taxonomies. A model for personal assistance in complex information spaces Javier Pereira Kurt Englmeier Claudio Rojas doi:10.1002/meet.1450390132 Proceedings of the American Society for Information Science and Technology, Vol. 39, No. 1. (2002), pp. 292-297. 2006-08-01T10:12:01-00:00 2002 Proceedings of the American Society for Information Science and Technology 39 1 292 297 digital-library personalization http://www.citeulike.org/user/brusilovsky/article/494291 <i>(2005), pp. 23-31.</i> Spotlight browsing of resource archives Paul Mulholland Trevor Collins Zdenek Zdrahal doi:10.1145/1083356.1083362 (2005), pp. 23-31. 2006-02-06T17:28:52-00:00 2005 23 31 ACM Press digital-library navigation navigation-support ontology http://www.citeulike.org/user/brusilovsky/article/374169 <i>Proceedings of the 2002 ACM conference on Computer supported cooperative work (2002), pp. 116-125.</i><br /><br />Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain. On the Recommending of Citations for Research Papers Sean Mcnee Istvan Albert Dan Cosley Prateep Gopalkrishnan Shyong Lam Al Rashid Joseph Konstan John Ried Proceedings of the 2002 ACM conference on Computer supported cooperative work (2002), pp. 116-125. 2005-11-01T00:11:45-00:00 2002 Proceedings of the 2002 ACM conference on Computer supported cooperative work 116 125 academic-reference digital-library personalization recommender http://www.citeulike.org/user/brusilovsky/article/375907 <i>International Conference on Digital Libraries,Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries (2004), pp. 228-236.</i><br /><br />The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine Collaborative Filtering and Content-based Filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102,000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently. These results can be applied to develop recommender systems for other types of digital libraries. Enhancing Digital Libraries with TechLens+ Roberto Torres Sean Mcnee Mara Abel Joseph Konstan John Riedl International Conference on Digital Libraries,Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries (2004), pp. 228-236. 2005-11-01T21:29:27-00:00 2004 International Conference on Digital Libraries,Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries 228 236 academic-reference digital-library personalization recommender http://www.citeulike.org/user/brusilovsky/article/209018 <i>Library Management, Vol. 26, No. 4-5. (April 2005), pp. 206-217.</i> Towards personalization in digital libraries through ontologies Nuria Ferran Enric Mor Julia Minguillon doi:10.1108/01435120510596062 Library Management, Vol. 26, No. 4-5. (April 2005), pp. 206-217. 2005-05-23T20:51:42-00:00 2005 Library Management 0143-5124 26 4-5 206 217 Emerald Group Publishing Limited digital-library personalization http://www.citeulike.org/user/brusilovsky/article/532560 <i>(May 2003)</i><br /><br />Digital libraries are collections of information that have associated services delivered to user communities using a variety of technologies. The collections of information can be scientific, business or personal data, and can be represented as digital text, image, audio, video, or other media. This information can be digitised paper or born digital material and the services offered on such information can be varied, ranging from content operations to rights management and can be offered to individuals or user communities. Internet access has resulted in digital libraries that are increasingly used by diverse communities for diverse purposes, and in which sharing and collaboration have become important social elements. As digital libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, people expect more sophisticated services from their digital libraries. A traditional search function is normally an integral part of any digital library, but users’ frustrations with this increase as their needs become more complex and as the volume of information managed by digital libraries increases. Digital libraries must move from being passive, with little adaptation to their users, to being more proactive in offering and tailoring information for individuals and communities, and in supporting community efforts to capture, structure, and share knowledge. Digital libraries that are not personalised for individuals and/or communities will be seen as defaulting on their obligation to offer the best service possible. Just as people patronize stores in which they, and their preferences are known, and their needs anticipated, so too will they patronize digital libraries that remember them, and anticipate their needs. Personalisation and Recommender Systems in Digital Libraries Joint NSF-EU DELOS Working Group Report Micheline Beaulieu Pia Borlund Peter Brusilovsky Matthew Chalmers Clifford Lynch John Riedl Barry Smyth Umberto Straccia Elaine Toms (May 2003) 2006-03-07T17:51:26-00:00 2003 digital-library personalization recommender http://www.citeulike.org/user/brusilovsky/article/373702 <i>JCDL '02 (July 13-17 2002), pp. 65-73.</i> A Graph-based Recommender System for Digital Library Zan Huang Wingyan Chung Thian-Huat Ong Hsinchun Chen JCDL '02 (July 13-17 2002), pp. 65-73. 2005-10-31T19:14:02-00:00 2002 JCDL '02 65 73 digital-library recommender http://www.citeulike.org/user/brusilovsky/article/686655 <i>(2005), pp. 235-243.</i> In the company of readers: the digital library book as "practiced place" Nancy Kaplan Yoram Chisik doi:10.1145/1065385.1065439 (2005), pp. 235-243. 2006-06-06T12:53:45-00:00 2005 235 243 ACM Press annotation digital-library information-retrieval http://www.citeulike.org/user/brusilovsky/article/686550 <i>(2005), pp. 9-18.</i> Comprehensive personalized information access in an educational digital library Peter Brusilovsky Rosta Farzan Jae-Wook Ahn doi:10.1145/1065385.1065388 (2005), pp. 9-18. 2006-06-06T11:31:18-00:00 2005 9 18 ACM Press adaptive-search digital-library social-navigation user-study www http://www.citeulike.org/user/brusilovsky/article/686547 <i>(2000), pp. 19-26.</i> Effects of annotations on student readers and writers Joanna Wolfe doi:10.1145/336597.336620 (2000), pp. 19-26. 2006-06-06T11:27:52-00:00 2000 19 26 ACM Press annotation bookmarking digital-library http://www.citeulike.org/user/brusilovsky/article/274117 <i>(1997), pp. 131-140.</i> Annotation: from paper books to the digital library Catherine Marshall doi:10.1145/263690.263806 (1997), pp. 131-140. 2005-08-04T21:27:37-00:00 1997 131 140 ACM Press annotation digital-library information-retrieval http://www.citeulike.org/user/brusilovsky/article/383226 <i>(2005), pp. 801-810.</i> A personalized search engine based on web-snippet hierarchical clustering Paolo Ferragina Antonio Gulli doi:10.1145/1062745.1062760 (2005), pp. 801-810. 2005-11-08T04:29:29-00:00 2005 801 810 ACM Press adaptive-search digital-library social-navigation user-study www